>> Announcer: It's theCUBE! Covering the Virtual Vertica Big Data Conference 2020 brought to you by Vertica. >> Hi, everybody. Welcome back. You're watching theCUBE's coverage of the Vertica Virtual Big Data Conference. It was, of course, going to be in Boston at the Encore Hotel. Win big with big data with the new casino but obviously Coronavirus has changed all that. Our hearts go out and we are empathy to those people who are struggling. We are going to continue our wall-to-wall coverage of this conference and we're here with Larry Lancaster who's the founder and CTO of Zebrium. Larry, welcome to theCUBE. Thanks for coming on. >> Hi, thanks for having me. >> You're welcome. So first question, why did you start Zebrium? >> You know, I've been dealing with machine data a long time. So for those of you who don't know what that is, if you can imagine servers or whatever goes on in a data center or in a SAS shop. There's data coming out of those servers, out of those applications and basically, you can build a lot of cool stuff on that. So there's a lot of metrics that come out and there's a lot of log files that come. And so, I've built this... Basically spent my career building that sort of thing. So tools on top of that or products on top of that. The problem is that since at least log files are completely unstructured, it's always doing the same thing over and over again, which is going in and understanding the data and extracting the data and all that stuff. It's very time consuming. If you've done it like five times you don't want to do it again. So really, my idea was at this point with machine learning where it's at there's got to be a better way. So Zebrium was founded on the notion that we can just do all that automatically. We can take a pile of machine data, we can turn it into a database, and we can build stuff on top of that. And so the company is really all about bringing that value to the market. >> That's cool. I want to get in to that, just better understand who you're disrupting and understand that opportunity better. But before I do, tell us a little bit about your background. You got kind of an interesting background. Lot of tech jobs. Give us some color there. >> Yeah, so I started in the Valley I guess 20 years ago and when my son was born I left grad school. I was in grad school over at Berkeley, Biophysics. And I realized I needed to go get a job so I ended up starting in software and I've been there ever since. I mean, I spent a lot of time at, I guess I cut my teeth at Nedap, which was a storage company. And then I co-founded a business called Glassbeam, which was kind of an ETL database company. And then after that I ended up at Nimble Storage. Another company, EMC, ended up buying the Glassbeam so I went over there and then after Nimble though, which where I build the InfoSight platform. That's where I kind of, after that I was able to step back and take a year and a half and just go into my basement, actually, this is my kind of workspace here, and come up with the technology and actually build it so that I could go raise money and get a team together to build Zebrium. So that's really my career in a nutshell. >> And you've got Hello Kitty over your right shoulder, which is kind of cool >> That's right. >> And then up to the left you got your monitor, right? >> Well, I had it. It's over here, yeah. >> But it was great! Pull it out, pull it out, let me see it. So, okay, so you got that. So what do you do? You just sit there and code all night or what? >> Yeah, that's right. So Hello Kitty's over here. I have a daughter and she setup my workspace here on this side with Hello Kitty and so on. And over on this side, I've got my recliner where I basically lay it all the way back and then I pivot this thing down over my face and put my keyboard on my lap and I can just sit there for like 20 hours. It's great. Completely comfortable. >> That's cool. All right, better put that monitor back or our guys will yell at me. But so, obviously, we're talking to somebody with serious coding chops and I'll also add that the Nimble InfoSight, I think it was one of the best pick ups that HP, HPE, has had in a while. And the thing that interested me about that, Larry, is the ability that the company was able to take that InfoSight and poured it very quickly across its product lines. So that says to me it was a modern, architecture, I'm sure API, microservices, and all those cool buzz words, but the proof is in their ability to bring that IP to other parts of the portfolio. So, well done. >> Yeah, well thanks. Appreciate that. I mean, they've got a fantastic team there. And the other thing that helps is when you have the notion that you don't just build on top of the data, you extract the data, you structure it, you put that in a database, we used Vertica there for that, and then you build on top of that. Taking the time to build that layer is what lets you build a scalable platform. >> Yeah, so, why Vertica? I mean, Vertica's been around for awhile. You remember you had the you had the old RDBMS, Oracles, Db2s, SQL Server, and then the database was kind of a boring market. And then, all of a sudden, you had all of these MPP companies came out, a spade of them. They all got acquired, including Vertica. And they've all sort of disappeared and morphed into different brands and Micro Focus has preserved the Vertica brand. But it seems like Vertica has been able to survive the transitions. Why Vertica? What was it about that platform that was unique and interested you? >> Well, I mean, so they're the first fund to build, what I would call a real column store that's kind of market capable, right? So there was the C-Store project at Berkeley, which Stonebreaker was involved in. And then that became sort of the seed from which Vertica was spawned. So you had this idea of, let's lay things out in a columnar way. And when I say columnar, I don't just mean that the data for every column is in a different set of files. What I mean by that is it takes full advantage of things like run length and coding, and L file and coding, and block--impression, and so you end up with these massive orders of magnitude savings in terms of the data that's being pulled off of storage as well as as it's moving through the pipeline internally in Vertica's query processing. So why am I saying all this? Because it's fundamentally, it was a fundamentally disruptive technology. I think column stores are ubiquitous now in analytics. And I think you could name maybe a couple of projects which are mostly open source who do something like Vertica does but name me another one that's actually capable of serving an enterprise as a relational database. I still think Vertica is unique in being that one. >> Well, it's interesting because you're a startup. And so a lot of startups would say, okay, we're going with a born-in-the-cloud database. Now Vertica touts that, well look, we've embraced cloud. You know, we have, we run in the cloud, we run on PRAM, all different optionality. And you hear a lot of vendors say that, but a lot of times they're just taking their stack and stuffing it into the cloud. But, so why didn't you go with a cloud-native database and is Vertica able to, I mean, obviously, that's why you chose it, but I'm interested from a technologist standpoint as to why you, again, made that choice given all these other choices around there. >> Right, I mean, again, I'm not, so... As I explained a column store, which I think is the appropriate definition, I'm not aware of another cloud-native-- >> Hm, okay. >> I'm aware of other cloud-native transactional databases, I'm not aware of one that has the analytics form it and I've tried some of them. So it was not like I didn't look. What I was actually impressed with and I think what let me move forward using Vertica in our stack is the fact that Eon really is built from the ground up to be cloud-native. And so we've been using Eon almost ever since we started the work that we're doing. So I've been really happy with the performance and with reliability of Eon. >> It's interesting. I've been saying for years that Vertica's a diamond in the rough and it's previous owner didn't know what to do with it because it got distracted and now Micro Focus seems to really see the value and is obviously putting some investments in there. >> Yeah >> Tell me more about your business. Who are you disrupting? Are you kind of disrupting the do-it-yourself? Or is there sort of a big whale out there that you're going to go after? Add some color to that. >> Yeah, so our broader market is monitoring software, that's kind of the high-level category. So you have a lot of people in that market right now. Some of them are entrenched in large players, like Datadog would be a great example. Some of them are smaller upstarts. It's a pretty, it's a pretty saturated market. But what's happened over the last, I'd say two years, is that there's been sort of a push towards what's called observability in terms of at least how some of the products are architected, like Honeycomb, and how some of them are messaged. Most of them are messaged these days. And what that really means is there's been sort of an understanding that's developed that that MTTR is really what people need to focus on to keep their customers happy. If you're a SAS company, MTTR is going to be your bread and butter. And it's still measured in hours and days. And the biggest reason for that is because of what's called unknown unknowns. Because of complexity. Now a days, things are, applications are ten times as complex as they used to be. And what you end up with is a situation where if something is new, if it's a known issue with a known symptom and a known root cause, then you can setup a automation for it. But the ones that really cost a lot of time in terms of service disruption are unknown unknowns. And now you got to go dig into this massive mass of data. So observability is about making tools to help you do that, but it's still going to take you hours. And so our contention is, you need to automate the eyeball. The bottleneck is now the eyeball. And so you have to get away from this notion of a person's going to be able to do it infinitely more efficient and recognize that you need automated help. When you get an alert agent, it shouldn't be that, "Hey, something weird's happening. Now go dig in." It should be, "Here's a root cause and a symptom." And that should be proposed to you by a system that actually does the observing. That actually does the watching. And that's what Zebrium does. >> Yeah, that's awesome. I mean, you're right. The last thing you want is just another alert and it say, "Go figure something out because there's a problem." So how does it work, Larry? In terms of what you built there. Can you take us inside the covers? >> Yeah, sure. So there's really, right now there's two kinds of data that we're ingesting. There's metrics and there's log files. Metrics, there's actually sort of a framework that's really popular in DevOp circles especially but it's becoming popular everywhere, which is called Prometheus. And it's a way of exporting metrics so that scrapers can collect them. And so if you go look at a typical stack, you'll find that most of the open source components and many of the closed source components are going to have exporters that export all their stacks to Prometheus. So by supporting that stack we can bring in all of those metrics. And then there's also the log files. And so you've got host log files in a containerized environment, you've got container logs, and you've got application-specific logs, perhaps living on a host mount. And you want to pull all those back and you want to be able to associate this log that I've collected here is associated with the same container on the same host that this metric is associated with. But now what? So once you've got that, you've got a pile of unstructured logs. So what we do is we take a look at those logs and we say, let's structure those into tables, right? So where I used to have a log message, if I look in my log file and I see it says something like, X happened five times, right? Well, that event types going to occur again and it'll say, X happened six times or X happened three times. So if I see that as a human being, I can say, "Oh clearly, that's the same thing." And what's interesting here is the times that X, that X happened, and that this number read... I may want to know when the numbers happened as a time series, the values of that column. And so you can imagine it as a table. So now I have table for that event type and every time it happens, I get a row. And then I have a column with that number in it. And so now I can do any kind of analytics I want almost instantly across my... If I have all my event types structured that way, every thing changes. You can do real anomaly detection and incident detection on top of that data. So that's really how we go about doing it. How we go about being able to do autonomous monitoring in a way that's effective. >> How do you handle doing that for, like the Spoke app? Do you have to, does somebody have to build a connector to those apps? How do you handle that? >> Yeah, that's a really good question. So you're right. So if I go and install a typical log manager, there'll be connectors for different apps and usually what that means is pulling in the stuff on the left, if you were to be looking at that log line, and it will be things like a time stamp, or a severity, or a function name, or various other things. And so the connector will know how to pull those apart and then the stuff to the right will be considered the message and that'll get indexed for search. And so our approach is we actually go in with machine learning and we structure that whole thing. So there's a table. And it's going to have a column called severity, and timestamp, and function name. And then it's going to have columns that correspond to the parameters that are in that event. And it'll have a name associated with the constant parts of that event. And so you end up with a situation where you've structured all of it automatically so we don't need collectors. It'll work just as well on your home-grown app that has no collectors or no parsers to find or anything. It'll work immediately just as well as it would work on anything else. And that's important, because you can't be asking people for connectors to their own applications. It just, it becomes now they've go to stop what they're doing and go write code for you, for your platform and they have to maintain it. It's just untenable. So you can be up and running with our service in three minutes. It'll just be monitoring those for you. >> That's awesome! I mean, that is really a breakthrough innovation. So, nice. Love to see that hittin' the market. Who do you sell to? Both types of companies and what role within the company? >> Well, definitely there's two main sort of pushes that we've seen, or I should say pulls. One is from DevOps folks, SRE folks. So these are people who are tasked with monitoring an environment, basically. And then you've got people who are in engineering and they have a staging environment. And what they actually find valuable is... Because when we find an incident in a staging environment, yeah, half the time it's because they're tearing everything up and it's not release ready, whatever's in stage. That's fine, they know that. But the other half the time it's new bugs, it's issues and they're finding issues. So it's kind of diverged. You have engineering users and they don't have titles like QA, they're Dev engineers or Dev managers that are really interested. And then you've got DevOps and SRE people there (mumbles). >> And how do I consume your product? Is the SAS... I sign up and you say within three minutes I'm up and running. I'm paying by the drink. >> Well, (laughs) right. So there's a couple ways. So, right. So the easiest way is if you use Kubernetes. So Kubernetes is what's called a container orchestrator. So these days, you know Docker and containers and all that, so now there's container orchestrators have become, I wouldn't say ubiquitous but they're very popular now. So it's kind of on that inflection curve. I'm not exactly sure the penetration but I'm going to say 30-40% probably of shops that were interested are using container orchestrators. So if you're using Kubernetes, basically you can install our Kubernetes chart, which basically means copying and pasting a URL and so on into your little admin panel there. And then it'll just start collecting all the logs and metrics and then you just login on the website. And the way you do that is just go to our website and it'll show you how to sign up for the service and you'll get your little API key and link to the chart and you're off and running. You don't have to do anything else. You can add rules, you can add stuff, but you don't have to. You shouldn't have to, right? You should never have to do any more work. >> That's great. So it's a SAS capability and I just pay for... How do you price it? >> Oh, right. So it's priced on volume, data volume. I don't want to go too much into it because I'm not the pricing guy. But what I'll say is that it's, as far as I know it's as cheap or cheaper than any other log manager or metrics product. It's in that same neighborhood as the very low priced ones. Because right now, we're not trying to optimize for take. We're trying to make a healthy margin and get the value of autonomous monitoring out there. Right now, that's our priority. >> And it's running in the cloud, is that right? AWB West-- >> Yeah, that right. Oh, I should've also pointed out that you can have a free account if it's less than some number of gigabytes a day we're not going to charge. Yeah, so we run in AWS. We have a multi-tenant instance in AWS. And we have a Vertica Eon cluster behind that. And it's been working out really well. >> And on your freemium, you have used the Vertica Community Edition? Because they don't charge you for that, right? So is that how you do it or... >> No, no. We're, no, no. So, I don't want to go into that because I'm not the bizdev guy. But what I'll say is that if you're doing something that winds up being OEM-ish, you can work out the particulars with Vertica. It's not like you're going to just go pay retail and they won't let you distinguish between tests, and prod, and paid, and all that. They'll work with you. Just call 'em up. >> Yeah, and that's why I brought it up because Vertica, they have a community edition, which is not neutered. It runs Eon, it's just there's limits on clusters and storage >> There's limits. >> But it's still fully functional though. >> So to your point, we want it multi-tenant. So it's big just because it's multi-tenant. We have hundred of users on that (audio cuts out). >> And then, what's your partnership with Vertica like? Can we close on that and just describe that a little bit? >> What's it like. I mean, it's pleasant. >> Yeah, I mean (mumbles). >> You know what, so the important thing... Here's what's important. What's important is that I don't have to worry about that layer of our stack. When it comes to being able to get the performance I need, being able to get the economy of scale that I need, being able to get the absolute scale that I need, I've not been disappointed ever with Vertica. And frankly, being able to have acid guarantees and everything else, like a normal mature database that can join lots of tables and still be fast, that's also necessary at scale. And so I feel like it was definitely the right choice to start with. >> Yeah, it's interesting. I remember in the early days of big data a lot of people said, "Who's going to need these acid properties and all this complexity of databases." And of course, acid properties and SQL became the killer features and functions of these databases. >> Who didn't see that one coming, right? >> Yeah, right. And then, so you guys have done a big seed round. You've raised a little over $6 million dollars and you got the product market fit down. You're ready to rock, right? >> Yeah, that's right. So we're doing a launch probably, well, when this airs it'll probably be the day before this airs. Basically, yeah. We've got people... Like literally in the last, I'd say, six to eight weeks, It's just been this sort of pique of interest. All of a sudden, everyone kind of gets what we're doing, realizes they need it, and we've got a solution that seems to meet expectations. So it's like... It's been an amazing... Let me just say this, it's been an amazing start to the year. I mean, at the same time, it's been really difficult for us but more difficult for some other people that haven't been able to go to work over the last couple of weeks and so on. But it's been a good start to the year, at least for our business. So... >> Well, Larry, congratulations on getting the company off the ground and thank you so much for coming on theCUBE and being part of the Virtual Vertica Big Data Conference. >> Thank you very much. >> All right, and thank you everybody for watching. This is Dave Vellante for theCUBE. Keep it right there. We're covering wall-to-wall Virtual Vertica BDC. You're watching theCUBE. (upbeat music)

(dramatic orchestral music) >> Hey, welcome back everybody. Jeff Frick, here, at theCUBE. We're at the Palo Alto studios taking a very short break in the middle of the crazy fall conference season. We'll be back on the road again next week. But we're excited to take an opportunity to take a breath. Again, meet new companies, have CUBE conversations here in the studio, and we're really excited to have our next guest. He's Apurva Dave, the CMO of Sysdig. Apurva, great to see you. >> Thanks, Jeff, thanks for having me here. >> Yea, welcome, happy Friday. >> Appreciate it, happy Friday, always worth it. >> So give us kind of the 101 on Sysdig. >> Yep, Sysdig is a really cool story. It is founded by a gentleman named Loris Degioanni. And, I think the geeks in your audience will probably know Loris in a heartbeat because he was one of the co-creators of a really famous open source project called Wireshark. It's at 20 million users worldwide, for network forensics, network visibility, troubleshooting, all that great stuff. And, way back when, in 2012, Loris realized what cloud and containers were doing to the market and how people build applications. And he stepped back and said, "We're going to need "a totally new way to monitor "and secure these applications." So he left all that Wireshark success behind, and he started another open source project, which eventually became Sysdig. >> Okay. >> Fast-forward to today. Millions of people are using the open source Sysdig and the sister project Sysdig Falco to monitor and secure these containerized applications. >> So what did Sysdig the company delineate itself from Sysdig the open source project? >> Well, you know, that's part of the challenge with open source, it's like part of your identity, right. Open source is who you are. And, what we've done is, we've taken Loris's vision and made it a reality, which is, using this open source technology and instrumentation, we can then build these enterprise class products on top for security monitoring and forensics at scales that the biggest banks in the world can use, governments can use, pharma, healthcare, insurance, all these large companies that need enterprise class products. All based on that same, original open source technology that Loris conceived so many years ago. >> So would you say, so the one that we see all the time and kind of use a base for the open source model, you kind of, Hortonworks, it's really pure, open source Hadoop. Then you have, kind of, Mapbar, you know, it's kind of proprietary on top of Hadoop. And then you have Cloudera. It's kind of open core with a wrapper. I mean, how does the open piece fit within the other pieces that you guys provide? >> That's really a really insightful question because Loris has always had a different model to open source, which is, you create these powerful open source projects that, on their own, will solve a particular problem or use case. For example, the initial Sysdig open source project is really good at forensics and troubleshooting. Sysdig Falco is really good at runtime container security. Those are useful in and of themselves. But then for enterprise class companies, you operate that at massive scale and simplicity. So we add powerful user interfaces, enterprise class management, auditing, security. We bundle that all on top. And that becomes this Cloud-Native intelligence platform that we sell to enterprise. >> And how do they buy that? >> You can, as subscription model. You can use it either as software as a service, where we operate it for you, or you can use it as on-premise software, where we deliver the bits to you and you deploy it behind your firewall. Both of those products are exactly the same functionally, and that's kind of the benefit we had as a younger company coming to market. We knew when we started, we'd need to deliver our software in both forms. >> Okay and then how does that map to, you know, Docker, probably the most broadly known container application, which rose and really disturbed everything a couple years ago. And then that's been disturbed by the next great thing, which is Kubernetes. So how do you guys fit in within those two really well-known pieces of the puzzle? >> Yeah, well you know, like we were talking about earlier, there's so much magic and stardust around Kubernetes and Docker and you just say it to an IT person anywhere and either they're working on Kubernetes, they're thinking about working on Kubernetes, or they're wondering when they can get to working on Kubernetes. The challenge becomes that, once the stardust wears off, and you realize that yeah, this thing is valuable, but there's a lot of work to actually implementing it and operationalizing it, that's when your customers realize that their entire life is going to be upended when they implement these new technologies and implement this new platform. So that's where Sysdig and other products come in. We want to help those customers actually operationalize that software. For us, that's solving the huge gaps around monitoring, security, network visibility, forensics, and so on. And, part of my goal in marketing, is to help the customers realize that they're going to need all these capabilities as they start moving to Kubernetes. >> Right, certainly, it's the hot topic. I mean, we were just at VMworld, we've been covering VMworld forever, and both Pat and Sanjay had Kubernetes as parts of their keynotes on day one and day two. So they're all in, as well, all time for Amazon, and it goes without saying with Google. >> Yeah, so it's funny is, we released initial support for Kubernetes, get this, back in 2015. And, this was the point where, basically the world hadn't yet really, they didn't really know what Kubernetes was. >> Unless they watched theCUBE. >> Unless they watched-- >> They had Craig Mcklecky-- >> Okay, alright. >> On Google cloud platform next 2014. I looked it up. >> Awesome. Very nice-- >> Told us, even the story of the ship wheel and everything. But you're right, I don't think that many people were there. It was at Mission Bay Conference Center, which is not where you would think a Google conference would be. It's a 400 person conference facility. >> Exactly, and I think this year, CubeCon is probably going to be 7,000 people. Shows you a little bit of the growth of this industry. But, even back in 2015, we kind of recognized that it wasn't just about containers, but it was about the microservices that you build on top on containers and how you control those containers. That's really going to change the way enterprises build software. And that's been a guiding principle for us, as we've built out the company and the products. >> Well, way to get ahead of the curve, I love it. So, I see it of more of a philosophical question on an open source company. It's such an important piece of the modern software world, and you guys are foundationally built on that, but I always think about when you're managing your own resources. You know, how much time do you enable the engineers to spend on the open source piece of the open source project, and how much, which is great, and they get a lot of kudos in the ecosystem, and they're great contributors, and they get to speak at conferences, and it's good, it's important. Versus how much time they need to spend on the company stuff, and managing those two resource allocations, 'cause they're very different, they're both very important, and in a company, like Sysdig, they're so intimately tied together. >> Yeah, that last point to me is the biggest driver. I think some companies deal with open source as a side project that gives engineers an outlet to do some fun, interesting things they wouldn't otherwise do. For a company like Sysdig, open source is core to what we do. We think of these two communities that we serve, the open source community and the enterprise community. But it's all based on the same technology. And our job in this mix is to facilitate the activity going on in both of these communities in a way that's appropriate for how those communities want to operate. I think most people understand how an enterprise, you know, a commercial enterprise community wants to operate. They want Sysdig to have a roadmap and deliver on that roadmap, and that's all well and good. That open source element is really kind of new and challenging. Our model has always been that the core open source technology fuels our enterprise business, and what we need to do is put as much energy as we can into the open source, such that the community is inspired to interact with us, experiment, and give back. And if we do it right, two things happen. We see massive contribution from the community, the community might even take over our open source projects. We see that happening with Sysdig Falco right now. For us, our job then is to sit back, understand how that community is innovating, and how we can add value on top of it. So coming back all the way to your question around engineers and what they should be doing, step one, always contribute to the open source. Make our open source better, so that the community is inspired to interact with us. And then from there, we'll leverage all that goodness in a way that's right for our enterprise community. >> So really getting in almost like a flywheel effect. Just investing in that core flywheel and then spin off all kinds of great stuff. >> You got it, you know, my motto's always been like, if the open source is this thing off to the side, that you're wondering, oh, should our engineers be working on it, or shouldn't they, it's going to be a tough model to sustain long-term. There has to be an integrated value to your overall organization and you have to recognize that. And then, resource it appropriately. >> Right, so let's kind of come up to the present. You guys just had a big round of funding, congratulations. >> Yep, thank you. >> So you got some new cash in the bank. So what's next for Sysdig? Now you got this new powder, if you will, so what's on the horizon, where are you guys going next? Where are you taking the company forward? >> Great question, so, we just raised a $68.5 million Series D round, led by Inside Ventures and follow-on investors from our previous investors, Accel and Bane. 68.5 doesn't happen overnight. It's certainly been a set of wins since Loris first introduced those open source projects to releasing our monitoring product, adding our security product. In fact, earlier this year, we brought on a very experienced CEO, Suresh Vasudevan, who was the previous CEO of Nimble Storage, as a partner to Loris, so that they could grow the business together. Come this summer, we're having massive success. It feels like we've hit a hockey stick late last year, where we signed up some of the largest investment banks in the world, large government organizations, Fortune 500s, all the magic is happening that you hope for, and all of a sudden, we found these investors knocking at our door, we weren't actually even out looking for funds, and we ended up with an over-subscribed round. >> Right. >> So our next goal, like what are you going to do with all that money, is first of all, we're moving to a phase where, it's not just about the product, but it's about the overall experience with Sysdig the company. We're really building that out, so that every enterprise has an incredible experience with our product and the company itself, so that they're just, you know, amazed with what Sysdig did to help make Cloud-Native a reality. >> That's great and you got to bring in an extra investor, like in a crunch phase, you guys haven't had that many investors in the company, relatively a small number of participants. >> It's been very tightly held, and we like it that way. We want to keep out community small and tight. >> Well, Apurva, exciting times, and I'm sure you're excited to have some of that money to spend on marketing going forward. >> Well, we'll do our part. >> Well, thanks for sharing your story, and have a great weekend. I'm happy it's Friday, I'm sure you are, too. >> Thanks so much, have a great weekend. Thanks for having me. >> He's Apurva, I'm Jeff, you're watching theCUBE. It's theCUBE conversation in Palo Alto, we'll be back on the road next week, so keep on watching. See you next time. (dramatic orchestral music)

>> Announcer: Live from Las Vegas it's theCUBE covering VMWorld 2017. Brought to you by VMware and its ecosystem partners. >> Okay, welcome back everyone. Live CUBE coverage here at VMWorld 2017. Three days, we're on our third day of VMWorld, always a great tradition, our eighth year. I'm John Furrier with theCUBE co-hosted by Dave Vellante of Wikibon and our next guest is Keegan Riley, vice president and general manager of North American storage at HP Enterprise. Welcome to theCUBE. >> Thank you, thanks for having me. >> Thanks for coming on, love the pin, as always wearin' that with flair. Love the logo, always comment on that when I, first I was skeptical on it, but now I love it, but, HP doing great in storage with acquisitions of SimpliVity and Nimble where you had a good run there. >> Keegan: Absolutely. >> We just had a former HPE entrepreneur now on doing a storage startup, so we're familiar with he HPE storage. Good story. What's the update now, you got Discover in the books, now you got the Madrid coming up event. Software to find storage that pony's going to run for a while. What's the update? >> Yeah, so appreciate the time, appreciate you having me on. You know, the way that we're thinking about HPE's storage it's interesting, it's the company is so different, and mentioned to you guys when we were talking before that I actually left HP to come to Nimble, so in some ways I'm approaching the gold pin for a 10 year anniversary at HP. But the-- >> And they retro that so you get that grand floated in. >> Oh, absolutely, absolutely, vacation time carries over it's beautiful. But the HPE storage that I'm now leading is in some ways very different from the HP storage that I left sic years ago and the vision behind HPE's storage is well aligned with the overall vision of Hewlett-Packard Enterprise, which is we make hybrid IT simple, we power the intelligent edge, and we deliver the services to empower organizations to do this. And the things that we were thinking about at Nimble and the things that we're thinking about as kind of a part of HPE are well aligned with this. So, our belief is everyone at this conference cares about whether it's software defined, whether it's hybrid converge, whether it's all flash so on and so forth, but in the real world what clients tend to care about is kind of their experience and we've seen this really fundamental shift in how consumers think about interacting with IT in general. The example I always give is you know I've been in sales my whole career, I've traveled a lot and historically 15 years ago when I would go to a new city, you know, I would land and I would jump on a airport shuttle to go rent a car and then I would pull out a Thomas Guide and I would go to cell C3 and map out my route to the client and things like that. And so I just expected that if I had a meeting at 2:00 p.m., I needed to land at 10:00 a.m., to make my way to, that was just my experience. Cut to today, you know, I land and I immediately pull out my iPhone and hail an Uber and you know reserve an Airbnb when I get there and I, for a 2:00 p.m. meeting I can land at 1:15 and I know Waze is going to route me around traffic to get there. So, my experience as a consumer has fundamentally changed and that's true of IT organizations and consumers within those organizations. So, IT departments have to adapt to that, right? And so a kind of powering this hybrid IT experience and servicing clients that expect immediacy is what we're all about. >> Okay, so I love that analogy. In fact when we were at HP Discover we kind of had this conversation, so as you hailed that Uber, IT wants self driving storage. >> Keegan: Absolutely. >> So, bring that, tie that back, things that we talk a lot about in kind of a colorful joking way, but that is the automation goal of storage is to be available. We talk about edge, unstructured data, moving compute to the edge, it's nuanced now, storage and compute all this where they go through software. Self driving storage means something, and it's kind of a joke on one hand, but what does it actually mean for an IT guy? >> No, that's a great question and this is exactly the way that we think about it. An the self driving car analogy is a really powerful one, right? And so the way we think about this, we're delivering a predictive cloud platform overall and notice that's not a predictive cloud storage conversation and it's a big part of why it made a ton of sense for Nimble storage to become a part of HPE. We brought to bear a product called InfoSight that you might be familiar with. The idea behind InfoSight is in a cloud connected world the client should never know about what's going on in their infrastructure than we do. So, we view every system as being at the edge of our network and for about seven years now we've been collecting a massive amount of information about infrastructure, about 70 million telemetry points per day per system that's coming back to us. So, we have a massive anonymized dataset about infrastructure. So, we've been collecting all of the sensor data in the same way that say Uber or Tesla has been collecting sensor data from cars, right, and the next step kind of the next wave of innovation, if you will, is, okay it's great that you've collected this sensor data, now what do you do with it? Right? And so we're starting to think about how do you put actuators in place so that you can have an actual self managing data center. How can you apply a machine learning and global kind of corelation in a way that actually applies artificial intelligence to the data center and makes it truly touchless and self managing and self healing and so on and so forth. >> So, that vision alone is when, well, I'm sure when you pitched that to Meg, she was like,"Okay, that sounds good, "let's buy the company." But as well, there was another factor, which was the success that Nimble was having. A major shift in the storage market and you can see it walking around here is that over the last five, seven years there's been a shift from the storage specialist expert at managing LUNs and deploying and tuning, to the sort of generalist because people realize, look, there's no competitive advantage. So, talk about that and how the person to whom you've sold and your career has changed. >> Yeah, no, absolutely, it's a great point. And I think it's in a lot of ways it goes to, you're right, obviously Meg and Antonio saw a lot of value in Nimble Storage. The value that we saw as Nimble Storage is as a standalone storage company with kind of one product to sell. You know there's a saying in sales that if you're a hammer everything looks like a nail, right. And so, it's really cool that we could go get on a whiteboard and explain why the Castle file system is revolutionary and delivers superior IOPs and so on and so forth, but the conversation is shifting to more of a solutions conversation. It moves to how do I deliver actual value and how do I help organizations drive revenue and help them distinguish themselves from their competitors leveraging digital transformation. So, being a part of a company that has a wide portfolio and applying a solutions sales approach it's game changing, right. Our ability to go in and say, "I don't want to tell you about the Nimble OS, "I want to hear from you what your challenges are "and then I'm going to come back to you with a proposal "to help you solve those challenges." It's exciting for our sales teams, frankly, because it changes our conversations that makes us more consultative. >> Alright, talk about the some of the-- >> Value conversations. >> Talk about the sales engagement dynamic with the buyer of storage, especially you mentioned in the old days, now new days. A new dynamic's emerging we've identified on theCUBE past couple days and I'll just kind of lay it out for you and I want you to get a reaction. I'm the storage buyer of old, now I'm the modern guy, I got to know all the ins and outs of speeds and feeds against all the competitors, but now there's a new overlay on top of which is a broader picture across the organization that has compute, that has edge, so I feel more, not deluded from storage, but more holistic around other things, so I have to balance both worlds. I got to balance the, I got to know and nail the storage equation. >> Yeah. >> Okay, at well as know the connection points with how it all works, kind of almost as an OS. How do you engage in that conversation? 'Cause it's hard, right? 'Cause storage you go right into the weeds, speeds and feeds under the hood, see our numbers, we're great, we do all this stuff. But now you got to say wait a minute, but in a VM environment it's this, in a cloud it's like this and there's a little bit of bigger picture, HCI or whatever that is. How do you deal with that? >> No, absolutely, and I think that's well said. I mean, I think the storage market historically has always been sort of, alright, do you want Granny Smith apples or red delicious apples? It always sort of looked the same and it was just about I can deliver x number of IOPs and it became a speeds and feeds conversation. Today, it's not just not apples to apples, it's like you prefer apples, pineapples, or vacuum cleaners. Like, there's so many different ways to solve these challenges and so you have to take the conversation to a higher level, right. It has to be a conversation about how do you deliver value to businesses? And I think, I hear-- >> It gets confusing to the buyers, too, because they're being bombarded with a lot of fudd and they still got to check the boxes on all the under the hood stuff, the engine's got to work. >> And they come to VMWorld and every year there's 92 new companies that haven't heard of before that are pitching them on, hey, I solve your problems. I think what I'm hearing from clients a lot is they don't necessarily want to think about the storage, they don't want to think about do I provision RAID 10 or RAID five and do I manage this aggregate in this way or that way, they don't want to think about, right. So, I think this is why you're seeing the success of these next generation platforms that are radically simple to implement, right, and in some ways at Nimble, wen we were talking to some of these clients to have sort of a legacy approach to storage where you got like a primary LUN administrator, there's nothing wrong with that job, it's a great job and I have friends who do that job, but a lot of companies are now shifting to more of a generalist, I manage applications and I manage you know-- >> John: You manage a dashboard console. >> Exactly, yeah, so you have to make it simple and you have to make it you don't have to think about those things anymore. >> So, in thinking about your relationship over the years with VMware, as HP, you are part of the cartel I call it, the inner circle, you got all the APIs early, all the, you know, the CDKs or SDKs early. You know, you were one of the few. You, of course EMC, NetApp, all the big storage players, couple of IBM, couple others. Okay, and then you go to Nimble, you're a little guy, and it's like c'mon hey let's partner! Okay and so much has changed now that you're back at HPE, how has that, how is it VMware evolved from a ecosystem partner standpoint and then specifically where you are today with HPE? >> That's a great question and I remember the early days at Nimble when you know we were knocking on the door and they were like, "Who are you again? "Nimble who?" And we're really proud of sort of the reputation that we've earned inside of VMware, they're a great partner and they've built such a massive ecosystem, and I mean this show is incredible, right. They're such a core part of our business. At Nimble I feel like we earned sort of a seat at that table in some ways through technology differentiation and just grit and hustle, right. We kind of edged our way into those conversations. >> Dave: Performance. >> And performance. And we started to get interesting to them from a strategic perspective as just Nimble Storage. Now, as a part of HPE, HPE was, and in some ways as a part of HPE you're like, "Oh, that was cute." We thought we were strategic to VMware, now we actually are very strategic to VMware and the things that we're doing with them. From an innovation perspective it's like just throwing fuel on the fire, right. So, we're doubling down on some of the things we're doing around like VM Vision and InfoSight, our partnership with Visa and on ProLiant servers, things like that, it's a great partnership. And I think the things that VMware's announced this week are really exciting. >> Thank you, great to see you, and great to have you on theCUBE. >> Thank you so much. >> I'll give you the last word. What's coming up for you guys and HP storage as the vice president general manager, you're out there pounding the pavement, what should customers look for from you guys? >> No, I appreciate that. There's a couple things. So, first and foremost are R&D budget just got a lot bigger specifically around InfoSight. So, you'll see InfoSight come to other HPE products, 3PAR, ProLiant servers so on and so forth and InfoSight will become a much more interesting cloud based management tool for proactive wellness in the infrastructure. Second, you'll see us double down on our channel, right. So, the channel Nimble's always 100% channel, SimpliVity was 100% channel, HPE Storage is going to get very serious about embracing the channel. And third, we're going to ensure that the client experience remains top notch. The NPS score of 85 that Nimble delivered we're really proud of that and we're going to make sure we don't mess that up for our clients. >> You know it's so funny, just an observation, but I worked at HP for nine years in the late '80s, early '90s and then I watched and been covering theCUBE for over seven years now, storage is always like the power engine of HPE and no matter what's happening it comes back down to storage, I mean, the earnings, the results, the client engagements, storage has moved from this corner kind of function to really strategic. And it continues that way. Congratulations. >> Thank you so much. Appreciate the time. >> Alright, it's theCUBE. Coming up Pat Gelsinger on theCUBE at one o'clock. Stay with us. Got all the great guests and alumni and also executives from VMware coming on theCUBE. I'm John Furrier, Dave Vellante. We'll be right back with more live coverage after this short break.

>> Announcer: Live! From Las Vegas. It's theCUBE. Covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Welcome back everyone. We are here live in Las Vegas for SiliconANGLE's Cube exclusive coverage of three days of wall to wall interviews here at HPE Discover 2017. I'm John Furrier, your host with Dave Vellante, cohost. And our next two guest is Rod Bagg, VP of Analytics, Customer Support, Data Center, Infrastructure, HPE, formerly Nimble now HPE. and Paul Sabin, Senior Network and Infrastructure Manager at Baker Botts LLP. Guys, thanks for joining on theCUBE. >> Male Voices: Thanks for having us. >> So we talked before we came on camera about all the great stories Nimble obviously part of the fold here at HP Enterprise. Your customer stories. Let's get right into it. Tell your story about how Nimble put you out of a job. That's my favorite one. Go. >> Okay, so when I started or when we bought Nimble Storage, I was the senior storage engineer. So we purchased it, we brought it in-house. It was up within, within an hour, I was already starting carve out LUNs. At that point, I'm using the restful APIs to carve out the rest of the 200 LUNS that we needed. Presenting it to the hosts. And by the end of it, it ran itself. Between InfoSight and the fact that the product just is so easily automated, I kid you not, true story, at the end of the year when we were doing our self evaluations, my evaluation said, and congratulations, you don't need me anymore. My position is obsolete. And the management came back and said, Paul, you're absolutely right. We agree that we don't need this position anymore so we're going to promote you to the senior network infrastructure team. (John laughs) So I manage that now. >> So you got promoted. But this is a trend in automation. This is the DevOps, this is the programmable infrastructure world we're moving into with hybrid. >> Exactly. Rod, this is big deal. >> Yeah, yeah exactly. InfoSight as we see it plays a big role in that. Really the product is simple and being able to automate that. But InfoSight giving our customers sort of visibility at a very deep level into how the systems are performing. And what we do on the backend to drive availability really takes a lot of pain off of our customers. Not sure that we put everybody out of work but we certainly make life easier. So that they can focus on the business aspect. >> And you automate those tasks the way that really should be automated and that's a cool thing. >> Yup. >> Take a minute. I'll like you to take a minute just to explain what the product is and what you guys are doing. Just so we can get that out there as context. And then jump into some more stories. >> Yeah so from an InfoSight perspective? >> John: Yeah. >> So InfoSight is our predictive cloud analytics platform that uses machine learning to predict and prevent problems from occurring to our customers. So we're not disrupting their business. And so we collect somewhere in the order of, about maybe 25 million pieces of information from every array and the virtual environment. Everyday from every single array. All of that gets into a galactic database, where we have a team of data scientists working with our support engineers and our product engineers to build wellness rules. We have about eight hundred health checks that are really looking out at every part of the infrastructure for our customers and really avoiding issues for them. >> So you take the data across your entire install base. >> Rod: Yup. >> I'm sure you take care of the data so it's not all-- >> Rod: Oh yeah, it's all secure. >> Secure and nanomized. And then use that as predictive to prescribe or both or how are you-- >> Yeah both. So our real goal there is that if we know of an issue, that's either we found in our labs or maybe one customer has experienced it. Really, we're doing everything we possibly can to analyze that issue across the entire install base. So we're learning from peers. >> Male Voice: Yup. >> And applying those learnings across the install base and preventing other customers from hitting that issue. >> The system is autodidactic in this sense. It learns and then applies, is that right? >> Yeah. So we do machine learning. Semi-supervised in a lot of cases. So where we've seen and issue and we can train the models. And then it will look out for those sort of issue across the entire install. >> John: I like the notion of wellness. >> Yup. >> Brings some of the people we relate to. We also heard terms like self-driving storage. >> Yup. >> Layoff testers. >> Yeah. >> But this is again, the trend that really is needed. Share other stories that you have because this is really where IT is going as it moves to a different kind of application and consumption model for you guys. >> Right so, well, kind of touching about what he was talking about, when you're as a storage guy, what's the number one thing that us storage guys have to do, is we have to prove that it's not the storage that's the problem. So usually, what happened was, in the old world, I would produce some statistics of, okay, and here's the IOPS that we're producing and here's the latency during this time. So based on this, it wasn't me, I don't know who it was. I'm just going to tell you it's not me. In the new world-- [John] That was the finger pointing world. >> Yes it was! >> The other guy got it. >> But with InfoSight, it's like hey, I can tell you but you're also welcome to go here as well. But let me show you VMM site where it's going to show you, not only what was happening at the storage. But let me take you all the way down to the host and then the VM and we're going to find this problem. And yeah, turns out sometimes it's going to be the VM that's all of a sudden taking whatever reason adding a huge amount of latency. And that, is something that, there's no more finger pointing in it anymore. All of a sudden, we're in the same team, it's like this kumbaya thing. >> That's awesome. It's good for the cohesiveness as a team. But also it's time savers too. When you reduce the steps to do things, you get your weekends back as you guys say before you came on camera. Tell the story about how you had to do all this work on the provisioning on the replication side, >> Sure. When we deployed the arrays, we decided it was business decision to go ahead and put the production arrays into our production data center and then we would do the DR at a later time. So I've got all of my data live, on production. And they say, okay, we're adding our Nimble storage at our DR site. Paul, how much replication bandwidth do we need? And so, same story. In the old world, you go and you pull your statistics from your replication technology, you put it in excel spreadsheet, you figure out, okay, here's my peaks and I just want to say, if we fall behind just a little bit, this is what we can do. And so usually what happens is, I say, guys, in my best guess, based on what I can see from my limited scope because my eyes are bleeding at this point. >> From the spreadsheet. You're in a spreadsheet right now. >> Paul: Yes, exactly. >> You're in spreadsheet hell. >> I'm in spreadsheet hell. And so what I do is, after about a weekend's worth of work, I put in this recommendation and I usually fluff it because I could be wrong in my statistics and so this is what I end up creating. >> You don't want to be under. You want to be over. >> Exactly, I'm always trying to do that. So the firm, I'm, hopefully this is, nobody's watching at the office, but sometimes they maybe overpaying for something because I just don't want to make that chance. In the new world, this is actually the coolest thing ever. So I'm on InfoSight and I go to this little dropdown, it's like the tool planner, okay, what's that? Where it's going to tell you what you need for bandwidth based on your actual real data. So then I'm pulling, like okay, based on this time, what is the replication if I want to do it every hour. And what if I want to do it every two hours? So then I just take that and I turn it into this report that I got to present to the executive team and they're like, oh my goodness, you have certainly stepped up. How many weekends did you use on this one? And you know, I'm not going to tell them it took me five minutes in InfoSight (John laughs) to be able to create this report. >> Now that they. >> But now they know. >> Cat's out, but you already got promoted. >> Oh that's true. >> Hey Rod, can you talk about the decision to acquire Nimble. What was the genesis. Obviously there's a portfolio component, tuck-ins, fill in some gaps. But there's this other sort of IP piece. Maybe take us back. >> Yeah, so certainly, there was the portfolio fit with the storage platform. So that was obviously a big part of it. I think the other obviously big part was InfoSight. So the idea that what we're doing there with our customers and approving the availability of the systems and the operational performance of the system and keeping a close eye on that to make sure it's optimized. So all that value prop around InfoSight was a big part of the decision I think. We are working on extending InfoSight into the HP product line. Starting with 3PAR so we are working already with that engineering team. To be able to bring some of these features out as quickly as we can into the 3PAR world as well. >> So what is that, from an engineering standpoint, is that sort of the requirement there is to point InfoSight at the data, the 3PAR data? >> Yeah exactly. So 3PAR does collect a lot of data already. >> Yeah sure do. >> So really, we're just pulling that data into our pipelines and so on within InfoSight and taking advantage of some of the machine learning and algorithms and so on that we already do. Things like DMVision, would be possible and so on in that environment as well if you're a 3PAR customer. >> It's interesting. Back in, maybe 10 years ago, 3PAR was sort of the gold standard of what we used to call the hero report. >> Rod: That's right, yup, yeah. People love that. >> Thin provisioning. What impact it was. >> Rod: Yup. How much you save, et cetera. And then that predated the whole big data analytics years right? >> Rod: Yeah, exactly. >> So when Nimble started, they could have started with that premise. Right around that time. >> Yeah, yup. >> I remember when I first saw it, I was like wow this is magic. >> Yeah exactly. That was the premise, was to really apply data science to all of that data that was coming in. Really transform the support experience for Nimble. And I think that's the other big element for HP as well. There's lots of that we do in our support organization that, to be honest, it's quite enviable, by a lot of storage and high tech vendors. >> You guys took a different approach. I think what's really notable for me, which I'm impressed with is, everyone talks about this but very few put into action, is making the user experience center, >> Rod: Yeah exactly. >> Of the value. I mean all of the things you talk about, the benefits, is really centered around your experience right. Saving you time, making your life easier, shifting the automation, that could be automated with the right things. And moving into higher value things. So Paul, what's your thoughts on this as it goes forward. This world is evolving. We're hearing the message here, simplifying, hybrid IT, you got cloud right on the doorstep, multiple clouds are going to be the endgame, we'll know all this, so all said and done. Whole new infrastructure is going to be out there. What's your view of how that user experience for the practitioners will evolve. What's your vision. How do you see it playing out. >> Rod: Be out of a job again. (Paul laughs) >> No, true story. The firm decided that they were going to bring us some people to help us look into what cloud we should, or how we should utilize the cloud because even from us, we're trying to keep ourselves agile as a law firm. Because if we can provide our services in a better, more meaningful and faster way, that gives us a competitive edge. So we brought in this team and they went over all of our IOPS and at the time it was under the different storage system so it took at least 20, 30 hours of my time to get all these numbers that they wanted. And then they created this report for us. Which I thought was really meaningful and valuable. The last line was, you should do cloud work, cloud makes sense. So that was it. Solid advice you know. Money well spent. (laughs) >> And that's what Meg's basically saying in the key note. The right mix of cloud versus on-prem. Certainly law firms have proprietary information and they want it secure. I guess my question really is, fundamentally is, a provocative one, I'd love to get your thoughts on. Serious question, you can laugh at at it a little bit but with AI bots coming, you can almost see these kinds of legal tasks being automated away. So, you might be, next promotion is taking over the firm. That's where big data can in. So how are you guys looking at that as a firm because I'm sure the lawyers are saying, hey you know what, I can shift my value to higher yield activities >> Paul: Exactly. >> Where that makes sense. You guys talk about that at all? >> We do. And I actually use the example of NASA. I really love NASA, I'm a huge fan. And NASA decide, they declared, we're going to go to Mars. We're going to do this. How are we going to do this? We have to let go of our operational stuff. We have to let go, I mean we can launch the shuttle all day long, we're comfortable with that. We can go into the space station, we're comfortable with that. But now, we've got to go new. And the way we have to do that is, we have to drop this stuff. Let's let other people do this. Let's let the InfoSight team start handling a lot of that work for me. And now, I'm asking my team, guys, I want you to start dreaming. Get out of the operational work. Start dreaming out loud. Let's figure out ways we can deliver value to our attorneys. >> Exactly. >> To free them. And let's let them just, again, take that same freedom, with the business intelligence and the machine learning, you're right that they're document management, which is their bread and butter, is their document production. Even that's getting scrutinized or transformed through this machine learning. And so, you could take this as a, as a way of saying no, there goes my job. Or you can say no, now I've got the opportunity to do something even better and cooler and really bring the value. >> And stretching. That's the whole stretch goal. Having that moonshot, in this case Mars. >> Paul: Mars right. >> It's the stretch and leverage right. >> Paul: Yes. >> That's the concept. How do you apply that to storage because now HP's got the composability, they got synergy. >> Paul: Yeah, yup. >> They have all kinds of. Now glue layer's kind of developing. We heard Antonio Neri in the press and analyst queue. We heard Meg Whitman talk about, you know, most her acquisitions have been in software, except for maybe one or two, over the past couple years, have been software. >> Paul: Yup. >> So, hardware, software kind of blending. >> Yeah. I think so, from the storage perspective certainly, I think that's happening. I think from the InfoSight perspective, where we see that going, is again, today when we put a lot of effort into our recommendation models. And that's an area that's very much in the deep data sciences realm. So when we come up with those recommendations, >> John: Umhmm. >> you know, we do things where we can prevent people from hitting issues and not just sort of happen automatically but some of these things are, something needs changing in their environment. So maybe, maybe there's a QoS policy that should be applied on the array to optimize performance because of some peak workload during Christmas, something of that nature. So that's still a last mile problem for us because you've got a human at the other end that's got to go in there and fix it and hopefully do it right and not ignore it and everything else. >> I can see the headline now, storage wellness coming to HP. >> Rod: Yeah exactly. >> But this is really interesting, comes with self-healing right. >> So that's where we want to go with that. That is really the thing we're working towards in the vision is, how do go and do that, change those QoS policies for the customer where we could inject, let's say, a change control within their change management system. They can go hit a button which we orchestrate that change for them. It's all documented and well controlled. >> It's not just storing the data, it's being data driven for the data being stored in the self crafting storage. >> Rod: Exactly, yeah, exactly. >> Rod, Paul thanks so much for sharing the stories and congratulations on the promotion. >> Thank you. >> And congratulations on InfoSight. You guys got great story there. >> But I never get promoted. (everyone laughs) >> Come in theCUBE, >> great story right. >> get promoted. >> Birds of a feather. >> Appreciate it. >> Thanks for having us. More live coverage here from theCUBE. Here at HP Discover 2017 after this short break. I'm John Furrier with Dave Vellante. We'll be right back. (lively music)