>> 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)

(upbeat music) >> Narrator: It's theCUBE, covering the Virtual Vertica Big Data Conference 2020. Brought to you by Vertica. >> Dave Vellante: Hello everyone, and welcome to theCUBE's exclusive coverage of the Vertica Virtual Big Data Conference. You're watching theCUBE, the leader in digital event tech coverage. And we're broadcasting remotely from our studios in Palo Alto and Boston. And, we're pleased to be covering wall-to-wall this digital event. Now, as you know, originally BDC was scheduled this week at the new Encore Hotel and Casino in Boston. Their theme was "Win big with big data". Oh sorry, "Win big with data". That's right, got it. And, I know the community was really looking forward to that, you know, meet up. But look, we're making the best of it, given these uncertain times. We wish you and your families good health and safety. And this is the way that we're going to broadcast for the next several months. Now, we want to unpack Colin Mahony's keynote, but, before we do that, I want to give a little context on the market. First, theCUBE has covered every BDC since its inception, since the BDC's inception that is. It's a very intimate event, with a heavy emphasis on user content. Now, historically, the data engineers and DBAs in the Vertica community, they comprised the majority of the content at this event. And, that's going to be the same for this virtual, or digital, production. Now, theCUBE is going to be broadcasting for two days. What we're doing, is we're going to be concurrent with the Virtual BDC. We got practitioners that are coming on the show, DBAs, data engineers, database gurus, we got a security experts coming on, and really a great line up. And, of course, we'll also be hearing from Vertica Execs, Colin Mahony himself right of the keynote, folks from product marketing, partners, and a number of experts, including some from Micro Focus, which is the, of course, owner of Vertica. But I want to take a moment to share a little bit about the history of Vertica. The company, as you know, was founded by Michael Stonebraker. And, Verica started, really they started out as a SQL platform for analytics. It was the first, or at least one of the first, to really nail the MPP column store trend. Not only did Vertica have an early mover advantage in MPP, but the efficiency and scale of its software, relative to traditional DBMS, and also other MPP players, is underscored by the fact that Vertica, and the Vertica brand, really thrives to this day. But, I have to tell you, it wasn't without some pain. And, I'll talk a little bit about that, and really talk about how we got here today. So first, you know, you think about traditional transaction databases, like Oracle or IMBDB tour, or even enterprise data warehouse platforms like Teradata. They were simply not purpose-built for big data. Vertica was. Along with a whole bunch of other players, like Netezza, which was bought by IBM, Aster Data, which is now Teradata, Actian, ParAccel, which was the basis for Redshift, Amazon's Redshift, Greenplum was bought, in the early days, by EMC. And, these companies were really designed to run as massively parallel systems that smoked traditional RDBMS and EDW for particular analytic applications. You know, back in the big data days, I often joked that, like an NFL draft, there was run on MPP players, like when you see a run on polling guards. You know, once one goes, they all start to fall. And that's what you saw with the MPP columnar stores, IBM, EMC, and then HP getting into the game. So, it was like 2011, and Leo Apotheker, he was the new CEO of HP. Frankly, he has no clue, in my opinion, with what to do with Vertica, and totally missed one the biggest trends of the last decade, the data trend, the big data trend. HP picked up Vertica for a song, it wasn't disclosed, but my guess is that it was around 200 million. So, rather than build a bunch of smart tokens around Vertica, which I always call the diamond in the rough, Apotheker basically permanently altered HP for years. He kind of ruined HP, in my view, with a 12 billion dollar purchase of Autonomy, which turned out to be one of the biggest disasters in recent M&A history. HP was forced to spin merge, and ended up selling most of its software to Microsoft, Micro Focus. (laughs) Luckily, during its time at HP, CEO Meg Whitman, largely was distracted with what to do with the mess that she inherited form Apotheker. So, Vertica was left alone. Now, the upshot is Colin Mahony, who was then the GM of Vertica, and still is. By the way, he's really the CEO, and he just doesn't have the title, I actually think they should give that to him. But anyway, he's been at the helm the whole time. And Colin, as you'll see in our interview, is a rockstar, he's got technical and business jobs, people love him in the community. Vertica's culture is really engineering driven and they're all about data. Despite the fact that Vertica is a 15-year-old company, they've really kept pace, and not been polluted by legacy baggage. Vertica, early on, embraced Hadoop and the whole open-source movement. And that helped give it tailwinds. It leaned heavily into cloud, as we're going to talk about further this week. And they got a good story around machine intelligence and AI. So, whereas many traditional database players are really getting hurt, and some are getting killed, by cloud database providers, Vertica's actually doing a pretty good job of servicing its install base, and is in a reasonable position to compete for new workloads. On its last earnings call, the Micro Focus CFO, Stephen Murdoch, he said they're investing 70 to 80 million dollars in two key growth areas, security and Vertica. Now, Micro Focus is running its Suse play on these two parts of its business. What I mean by that, is they're investing and allowing them to be semi-autonomous, spending on R&D and go to market. And, they have no hardware agenda, unlike when Vertica was part of HP, or HPE, I guess HP, before the spin out. Now, let me come back to the big trend in the market today. And there's something going on around analytic databases in the cloud. You've got companies like Snowflake and AWS with Redshift, as we've reported numerous times, and they're doing quite well, they're gaining share, especially of new workloads that are merging, particularly in the cloud native space. They combine scalable compute, storage, and machine learning, and, importantly, they're allowing customers to scale, compute, and storage independent of each other. Why is that important? Because you don't have to buy storage every time you buy compute, or vice versa, in chunks. So, if you can scale them independently, you've got granularity. Vertica is keeping pace. In talking to customers, Vertica is leaning heavily into the cloud, supporting all the major cloud platforms, as we heard from Colin earlier today, adding Google. And, why my research shows that Vertica has some work to do in cloud and cloud native, to simplify the experience, it's more robust in motor stack, which supports many different environments, you know deep SQL, acid properties, and DNA that allows Vertica to compete with these cloud-native database suppliers. Now, Vertica might lose out in some of those native workloads. But, I have to say, my experience in talking with customers, if you're looking for a great MMP column store that scales and runs in the cloud, or on-prem, Vertica is in a very strong position. Vertica claims to be the only MPP columnar store to allow customers to scale, compute, and storage independently, both in the cloud and in hybrid environments on-prem, et cetera, cross clouds, as well. So, while Vertica may be at a disadvantage in a pure cloud native bake-off, it's more robust in motor stack, combined with its multi-cloud strategy, gives Vertica a compelling set of advantages. So, we heard a lot of this from Colin Mahony, who announced Vertica 10.0 in his keynote. He really emphasized Vertica's multi-cloud affinity, it's Eon Mode, which really allows that separation, or scaling of compute, independent of storage, both in the cloud and on-prem. Vertica 10, according to Mahony, is making big bets on in-database machine learning, he talked about that, AI, and along with some advanced regression techniques. He talked about PMML models, Python integration, which was actually something that they talked about doing with Uber and some other customers. Now, Mahony also stressed the trend toward object stores. And, Vertica now supports, let's see S3, with Eon, S3 Eon in Google Cloud, in addition to AWS, and then Pure and HDFS, as well, they all support Eon Mode. Mahony also stressed, as I mentioned earlier, a big commitment to on-prem and the whole cloud optionality thing. So 10.0, according to Colin Mahony, is all about really doubling down on these industry waves. As they say, enabling native PMML models, running them in Vertica, and really doing all the work that's required around ML and AI, they also announced support for TensorFlow. So, object store optionality is important, is what he talked about in Eon Mode, with the news of support for Google Cloud and, as well as HTFS. And finally, a big focus on deployment flexibility. Migration tools, which are a critical focus really on improving ease of use, and you hear this from a lot of customers. So, these are the critical aspects of Vertica 10.0, and an announcement that we're going to be unpacking all week, with some of the experts that I talked about. So, I'm going to close with this. My long-time co-host, John Furrier, and I have talked some time about this new cocktail of innovation. No longer is Moore's law the, really, mainspring of innovation. It's now about taking all these data troves, bringing machine learning and AI into that data to extract insights, and then operationalizing those insights at scale, leveraging cloud. And, one of the things I always look for from cloud is, if you've got a cloud play, you can attract innovation in the form of startups. It's part of the success equation, certainly for AWS, and I think it's one of the challenges for a lot of the legacy on-prem players. Vertica, I think, has done a pretty good job in this regard. And, you know, we're going to look this week for evidence of that innovation. One of the interviews that I'm personally excited about this week, is a new-ish company, I would consider them a startup, called Zebrium. What they're doing, is they're applying AI to do autonomous log monitoring for IT ops. And, I'm interviewing Larry Lancaster, who's their CEO, this week, and I'm going to press him on why he chose to run on Vertica and not a cloud database. This guy is a hardcore tech guru and I want to hear his opinion. Okay, so keep it right there, stay with us. We're all over the Vertica Virtual Big Data Conference, covering in-depth interviews and following all the news. So, theCUBE is going to be interviewing these folks, two days, wall-to-wall coverage, so keep it right there. We're going to be right back with our next guest, right after this short break. This is Dave Vellante and you're watching theCUBE. (upbeat music)

>>Yeah, it's the queue covering the virtual vertical Big Data Conference 2020. Brought to you by vertical. >>Hi, everybody. And welcome to this cube special presentation of the vertical virtual Big Data conference. The Cube is running in parallel with Day One and day two of the vertical of Big Data event. By the way, the Cube has been every single big data event in It's our pleasure to be here in the virtual slash digital event as well. Gabriel Chapman is here. He's the director of Flash Blade Products Solutions Marketing at Pure Storage. Great to see you. Thanks for coming on. >>Great to see you too. How's it going? >>It's going very well. I mean, I wish we were meeting in Boston at the Encore Hotel, but, uh, you know, and hopefully we'll be able to meet it, accelerate at some point, future or one of the sub shows that you guys are doing the regional shows, but because we've been covering that show as well. But I really want to get into it. And the last accelerate September 2019 pure and vertical announced. Ah, partnership. I remember a joint being ran up to me and said, Hey, you got to check this out. The separation of compute and storage by EON mode now available on Flash Blade. So, uh and and I believe still the only company that can support that separation and independent scaling both on Prem and in the cloud. So I want to ask, what were the trends and analytical database and cloud led to this partnership? You know, >>realistically, I think what we're seeing is that there's been a kind of a larger shift when it comes to modern analytics platforms towards moving away from the traditional, you know, Hadoop type architecture where we were doing on and leveraging a lot of directors that storage primarily because of the limitations of how that solution was architected. When we start to look at the larger trends towards you know how organizations want to do this type of work on premises, they're looking at solutions that allow them to scale the compute storage pieces independently and therefore, you know, the flash blade platform ended up being a great solution to support America in their transition Tian mode. Leveraging essentially is an S three object store. >>Okay, so let's let's circle back on that you guys in your in your announcement of the flash blade, you make the claim that Flash Blade is the industry's most advanced file and object storage platform ever. That's a bold statement. So defend that What? >>I would like to go beyond that and just say, you know, So we've really kind of looked at this from a standpoint of, you know, as as we've developed Flash Blade as a platform and keep in mind, it's been a product that's been around for over three years now and has been very successful for pure storage. The reality is, is that fast file and fast object as a combined storage platform is a direction that many organizations are looking to go, and we believe that we're a leader in that fast object best file storage place in realistically, which we start to see more organizations start to look at building solutions that leverage cloud storage characteristics. But doing so on Prem for a multitude of different reasons. We've built a platform that really addresses a lot of those needs around simplicity around, you know, making things this year that you know, fast matters for us. Ah, simple is smart. Um we can provide, you know, cloud integrations across the spectrum. And, you know, there's a subscription model that fits into that as well. We fall that that falls into our umbrella of what we consider the modern day takes variance. And it's something that we've built into the entire pure portfolio. >>Okay, so I want to get into the architecture a little bit of flash blade and then understand the fit for, uh, analytic databases generally, but specifically for vertical. So it is a blade, so you got compute and network included. It's a key value store based system. So you're talking about scale out. Unlike, unlike, uh, pure is sort of, you know, initial products which were scale up, Um, and so I want on It is a fabric based system. I want to understand what that all means to take us through the architecture. You know, some of the quote unquote firsts that you guys talk about. So let's start with sort of the blade >>aspect. Yeah, the blade aspect of what we call the flash blade. Because if you look at the actual platform, you have, ah, primarily a chassis with built in networking components, right? So there's ah, fabric interconnect with inside the platform that connects to each one of the individual blades. Individual blades have their own compute that drives basically a pure storage flash components inside. It's not like we're just taking SSD is and plugging them into a system and like you would with the traditional commodity off the shelf hardware design. This is very much an engineered solution that is built towards the characteristics that we believe were important with fast filing past object scalability, massive parallel ization. When it comes to performance and the ability to really kind of grow and scale from essentially seven blades right now to 150 that's that's the kind of scale that customers are looking for, especially as we start to address these larger analytics pools. They are multi petabytes data sets, you know that single addressable object space and, you know, file performance that is beyond what most of your traditional scale up storage platforms are able to deliver. >>Yes, I interviewed cause last September and accelerate, and Christie Pure has been attacked by some of the competitors. There's not having scale out. I asked him his thoughts on that, he said Well, first of all, our flash blade is scale out. He said, Look, anything that adds complexity, you know we avoid. But for the workloads that are associated with flash blade scale out is the right sort of approach. Maybe you could talk about why that is. Well, >>realistically, I think you know that that approach is better when we're starting to work with large, unstructured data sets. I mean, flash blade is unique. The architected to allow customers to achieve superior resource utilization for compute and storage, while at the same time, you know, reducing significantly the complexity that has arisen around this kind of bespoke or siloed nature of big data and analytics solutions. I mean, we're really kind of look at this from a standpoint of you have built and delivered are created applications in the public cloud space of dress, you know, object storage and an unstructured data. And for some organizations, the importance is bringing that on Prem. I mean, we do see about repatriation coming on a lot of organizations as these data egress, charges continue to expand and grow, um, and then organizations that want even higher performance and what we're able to get into the public cloud space. They are bringing that data back on Prem They are looking at from a stamp. We still want to be able to scale the way we scale in the cloud. We still want to operate the same way we operate in the cloud, but we want to do it within control of our own, our own borders. And so that's, you know, that's one of the bigger pieces to that. And we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models? A zealous the benefits and efficiencies of scale that they're able to afford but allowing customers to do that with inside their own data center. >>So you're talking about the trends earlier. You have these cloud native databases that allowed of the scaling of compute and storage independently. Vertical comes in with eon of a lot of times we talk about these these partnerships as Barney deals of you know I love you, You love me. Here's a press release and then we go on or they're just straight, you know, go to market. Are there other aspects of this partnership that they're non Barney deal like, in other words, any specific engineering. Um, you know other go to market programs? Could you talk about that a little bit? Yeah, >>it's it's It's more than just that what we consider a channel meet in the middle or, you know, that Barney type of deal. It's realistically, you know, we've done some first with Veronica that I think, really Courtney, if they think you look at the architecture and how we did, we've brought to market together. Ah, we have solutions. Teams in the back end who are, you know, subject matter experts. In this space, if you talk to joy and the people from vertical, they're very high on our very excited about the partnership because it often it opens up a new set of opportunities for their customers to leverage on mode and get into some of the the nuance task specs of how they leverage the depot depot with inside each individual. Compute node in adjustments with inside their reach. Additional performance gains for customers on Prem and at the same time, for them, that's still tough. The ability to go into that cloud model if they wish to. And so I think a lot of it is around. How do we partner is to companies? How do we do a joint selling motions? How do we show up in and do white papers and all of the traditional marketing aspects that we bring to the market? And then, you know, joint selling opportunities exist where they are, and so that's realistically. I think, like any other organization that's going to market with a partner on MSP that they have, ah, strong partnership with. You'll continue to see us, you know, talking about are those mutually beneficial relationships and the solutions that we're bringing to the market. >>Okay, you know, of course, he used to be a Gartner analyst, and you go to the vendor side now, but it's but it's, but it's a Gartner analyst. You're obviously objective. You see it on, you know well, there's a lot of ways to skin the cat There, there their strengths, weaknesses, opportunities, threats, etcetera for every vendor. So you have you have vertical who's got a very mature stack and talking to a number of the customers out there who are using EON mode. You know there's certain workloads where these cloud native databases makes sense. It's not just the economics of scaling and storage independently. I want to talk more about that. There's flexibility aspect as well. But Vertical really has to play its its trump card, which is Look, we've got a big on premise state, and we're gonna bring that eon capability both on Prem and we're embracing the cloud now. There obviously have been there to play catch up in the cloud, but at the same time, they've got a much more mature stack than a lot of these other cloud native databases that might have just started a couple of years ago. So you know, so there's trade offs that customers have to make. How do you sort through that? Where do you see the interest in this? And and what's the sweet spot for this partnership? You know, we've >>been really excited to build the partnership with vertical A and provide, you know, we're really proud to provide pretty much the only on Prem storage platform that's validated with the yang mode to deliver a modern data experience for our customers together. You know, it's ah, it's that partnership that allows us to go into customers that on Prem space, where I think that there's still not to say that not everybody wants to go there, but I think there's aspects and solutions that worked very well there. But for the vast majority, I still think that there's, you know, the your data center is not going away. And you do want to have control over some of the many of the assets with inside of the operational confines. So therefore, we start to look at how do we can do the best of what cloud offers but on prim. And that's realistically, where we start to see the stronger push for those customers. You still want to manage their data locally. A swell as maybe even worked around some of the restrictions that they might have around cost and complexity hiring. You know, the different types of skills skill sets that are required to bring applications purely cloud native. It's still that larger part of that digital transformation that many organizations are going for going forward with. And realistically, I think they're taking a look at the pros and cons, and we've been doing cloud long enough where people recognize that you know it's not perfect for everything and that there's certain things that we still want to keep inside our own data center. So I mean, realistically, as we move forward, that's, Ah, that better option when it comes to a modern architecture that can do, you know, we can deliver an address, a diverse set of performance requirements and allow the organization to continue to grow the model to the data, you know, based on the data that they're actually trying to leverage. And that's really what Flash was built for. It was built for a platform that could address small files or large files or high throughput, high throughput, low latency scale of petabytes in a single name. Space in a single rack is we like to put it in there. I mean, we see customers that have put 150 flash blades into production as a single name space. It's significant for organizations that are making that drive towards modern data experience with modern analytics platforms. Pure and Veronica have delivered an experience that can address that to a wide range of customers that are implementing uh, you know, particularly on technology. >>I'm interested in exploring the use case. A little bit further. You just sort of gave some parameters and some examples and some of the flexibility that you have, um, and take us through kind of what the customer discussions are like. Obviously you've got a big customer base, you and vertical that that's on Prem. That's the the unique advantage of this. But there are others. It's not just the economics of the granular scaling of compute and storage independently. There are other aspects of take us through that sort of a primary use case or use cases. Yeah, you >>know, I mean, I could give you a couple customer examples, and we have a large SAS analyst company which uses vertical on last way to authenticate the quality of digital media in real time, You know, then for them it makes a big difference is they're doing their streaming and whatnot that they can. They can fine tune the grand we control that. So that's one aspect that that we address. We have a multinational car car company, which uses vertical on flash blade to make thousands of decisions per second for autonomous vehicle decision making trees. You know, that's what really these new modern analytics platforms were built for, um, there's another healthcare organization that uses vertical on flash blade to enable healthcare providers to make decisions in real time. The impact lives, especially when we start to look at and, you know, the current state of affairs with code in the Corona virus. You know, those types of technologies, we're really going to help us kind of get of and help lower invent, bend that curve downward. So, you know, there's all these different areas where we can address that the goals and the achievements that we're trying to look bored with with real time analytics decision making tools like and you know, realistically is we have these conversations with customers they're looking to get beyond the ability of just, you know, a data scientist or a data architect looking to just kind of driving information >>that we're talking about Hadoop earlier. We're kind of going well beyond that now. And I guess what I'm saying is that in the first phase of cloud, it was all about infrastructure. It was about, you know, uh, spin it up. You know, compute and storage is a little bit of networking in there. >>It >>seems like the next new workload that's clearly emerging is you've got. And it started with the cloud native databases. But then bringing in, you know, AI and machine learning tooling on top of that Ah, and then being able to really drive these new types of insights and it's really about taking data these bog this bog of data that we've collected over the last 10 years. A lot of that is driven by a dupe bringing machine intelligence into the equation, scaling it with either cloud public cloud or bringing that cloud experience on Prem scale. You know, across organizations and across your partner network, that really is a new emerging workloads. You see that? And maybe talk a little bit about what you're seeing with customers. >>Yeah. I mean, it really is. We see several trends. You know, one of those is the ability to take a take this approach to move it out of the lab, but into production. Um, you know, especially when it comes to data science projects, machine learning projects that traditionally start out as kind of small proofs of concept, easy to spin up in the cloud. But when a customer wants to scale and move towards a riel you know, derived a significant value from that. They do want to be able to control more characteristic site, and we know machine learning, you know, needs toe needs to learn from a massive amounts of data to provide accuracy. There's just too much data retrieving the cloud for every training job. Same time Predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking. You know, we see this. Ah, the visualization of Data Analytics is Tricia deployed is being on a continuum with, you know, the things that we've been doing in the long in the past with data warehousing, data Lakes, ai on the other end. But this way, we're starting to manifest it and organizations that are looking towards getting more utility and better elasticity out of the data that they are working for. So they're not looking to just build apps, silos of bespoke ai environments. They're looking to leverage. Ah, you know, ah, platform that can allow them to, you know, do ai, for one thing, machine learning for another leverage multiple protocols to access that data because the tools are so much Jeff um, you know, it is a growing diversity of of use cases that you can put on a single platform I think organizations are looking for as they try to scale these environment. >>I think it's gonna be a big growth area in the coming years. Gable. I wish we were in Boston together. You would have painted your little corner of Boston orange. I know that you guys have but really appreciate you coming on the cube wall to wall coverage. Two days of the vertical vertical virtual big data conference. Keep it right there. Right back. Right after this short break, Yeah.

hi everybody and welcome to this cube special presentation of the verdict of virtual Big Data conference the cube is running in parallel with day 1 and day 2 of the verdict big data event by the way the cube has been at every single big data event and it's our pleasure to be here in the virtual / digital event as well Gabriel Chapman is here is the director of flash blade product solutions marketing at pure storage gave great to see you thanks for coming on great to see you - how's it going it's going very well I mean I wish we were meeting in Boston at the Encore Hotel but you know and and hopefully we'll be able to meet it accelerate at some point you cheer or one of the the sub shows that you guys are doing the regional shows but because we've been covering that show as well but I really want to get into it and the last accelerate September 2019 pure and Vertica announced a partnership I remember a joint being ran up to me and said hey you got to check this out the separation of Butte and storage by a Eon mode now available on flash played so and and I believe still the only company that can support that separation and independent scaling both on permit in the cloud so Gabe I want to ask you what were the trends in analytical database and cloud that led to this partnership you know realistically I think what we're seeing is that there's been in kind of a larger shift when it comes to modern analytics platforms towards moving away from the the traditional you know Hadoop type architecture where we were doing on and leveraging a lot of direct attached storage primarily because of the limitations of how that solution was architected when we start to look at the larger trends towards you know how organizations want to do this type of work on premises they're looking at solutions that allow them to scale the compute storage pieces independently and therefore you know the flash play platform ended up being a great solution to support Vertica in their transition to Eon mode leveraging is essentially as an s3 object store okay so let's let's circle back on that you guys in your in your announcement of a flash blade you make the claim that flash blade is the industry's most advanced file and object storage platform ever that's a bold statement so defend that it's supposed to yeah III like to go beyond that and just say you know so we've really kind of looked at this from a standpoint of you know as as we've developed flash blade as a platform and keep in mind it's been a product that's been around for over three years now and has you know it's been very successful for pure storage the reality is is that fast file and fast object as a combined storage platform is a direction that many organizations are looking to go and we believe that we're a leader in that fast object of best file storage place in realistically would we start to see more organizations start to look at building solutions that leverage cloud storage characteristics but doing so on prem or multitude different reasons we've built a platform that really addresses a lot of those needs around simplicity around you know making things assure that you know vast matters for us simple is smart we can provide you know cloud integrations across the spectrum and you know there's a subscription model that fits into that as well we fall that that falls into our umbrella of what we consider the modern data experience and it's something that we've built into the entire pure portfolio okay so I want to get into the architecture a little bit of Flash blade and then better understand the fit for analytic databases generally but specifically Vertica so it is a blade so you got compute and a network included it's a key value store based system so you're talking about scale out unlike unlike viewers sort of you know initial products which were scale up and so I want to under in as a fabric base system I want to understand what that all mean so take us through the architecture you know some of the quote-unquote firsts that you guys talk about so let's start with sort of the blade aspect yeah the blade aspect meaning we call it a flash blade because if you look at the actual platform you have a primarily a chassis with built in networking components right so there's a fabric interconnect with inside the platform that connects to each one of the individual blades the individual blades have their own compute that drives basically a pure storage flash components inside it's not like we're just taking SSDs and plugging them into a system and like you would with the traditional commodity off-the-shelf hardware design this is a very much an engineered solution that is built towards the characteristics that we believe were important with fast file and fast object scalability you know massive parallelization when it comes to performance and the ability to really kind of grow and scale from essentially seven blades right now to a hundred and fifty that's that's the kind of scale that customers are looking for especially as we start to address these larger analytic spools they have multi petabyte datasets you know that single addressable object space and you know file performance that is beyond what most of your traditional scale-up storage platforms are able to deliver yes I interviewed cause last September and accelerate and and Christopher's been you know attacked by some of the competitors is not having a scale out I asked him his thoughts on that he said well first of all our Flash blade is scale-out and he said look anything that that that adds the complexity you know we avoid but for the workloads that are associated with Flash blade scale-out is the right sort of approach maybe you could talk about why that is well you know realistically I think you know that that approach is better when we're starting to learn to work with large unstructured data sets I mean flash plays uniquely architected to allow customers to achieve you know a superior resource utilization for compute and storage well at the same time you know reducing significantly the complexity that is arisen around these kind of bespoke or siloed nature of big data and analytic solutions I mean we really kind of look at this from a standpoint of you have built and delivered or created applications in the public cloud space that address you know object storage and and unstructured data and and for some organizations the importance is bringing that on Prem I mean we do seek repatriation that coming on on for a lot of organizations as these data egress charges continue to expand and grow and then organizations that want even higher performance in the what we're able to get into the public cloud space they are bringing that data back on Prem they are looking at from a standpoint we still want to be able to scale the way we scale on the cloud we still want to operate the same way we operate in the cloud but we want to do it within control of our own you know our own borders and so that's you know that's one of the bigger pieces to that is we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models as well as the benefits and efficiencies of scale that they're able to afford but allowing customers that do that with inside their own data center yes are you talking about the trends earlier you had these cloud native databases that allowed the scaling of compute and storage independently of Vertica comes in with eon of a lot of times we talk about these these partnerships as Barney deals of you know I love you you love me here's a press release and then we go on or they're just straight you know go to market are there other aspects of this partnership that are that are non Barney deal like in other words any specific you know engineering you know other go to market programs can you talk about that a little bit yeah it's it's it's more than just you know I then what we consider a channel meet in the middle or you know that Barney type of deal it's the realistically you know we've done some first with Vertica that I think are really important if they think you look at the architecture and how we do have we've brought this to market together we have solutions teams in the back end who are you know subject matter experts in this space if you talk to joy and the people from vertigo they're very high on or very excited about the partnership because it often it opens up a new set of opportunities for their customers to to leverage Eon mode and you know get into some of the the nuanced aspects of how they leverage the depot for Depot with inside each individual compute node and adjustments with inside there I reach additional performance gains for customers on Prem and at the same time for them there's still the ability to go into that cloud model if they wish to and so I think a lot of it is around how do we partner as two companies how do we do a joint selling motions you know how do we show up and and you know do white papers and all of the the traditional marketing aspects that we bring devote to the market and then you know joint selling opportunities as exists where they are and so that's realistically I think like any other organization that's going to market with a partner or an ISP that they have a strong partnership with you'll continue to see us you know talking about our chose mutually beneficial relationships and the solutions that we're bringing to the market okay you know of course he used to be a Gartner analyst and you go over to the vendor side now but as but as it but as a gardener analyst you're obviously objective you see it all you know well there's a lot of ways to skin a cat there are there are there are strengths weaknesses opportunities threats etc for every vendor so you have you have Vertica who's got a very mature stack and and talking to a number of the customers out there we're using Eon mode you know there's certain workloads where these cloud native databases make sense it's not just the economics of scaling compute and storage independently I want to talk more about that there's flexibility aspects as well but Vertica really you know has to play its trump card which is look we've got a big on-premise state and we're gonna bring that you know Eon capability both on Prem and we're embracing the cloud now they're obviously you have to they had to play catch-up in the cloud but at the same time they've got a much more mature stack than a lot of these other you know cloud native databases that might have just started a couple of years ago so you know so there's trade-offs that customers have to make how do you sort through that where do you see the interest in this and and and what's the sweet spot for this partnership you know we've been really excited to build the partnership with Vertica and we're providing you know we're really proud to provide pretty much the only on Prem storage platform that's validated with the vertical yawn mode to deliver a modern data experience for our customers together you know it's it's that partnership that allows us to go into customers that on Prem space where I think that they're still you know not to say that not everybody wants to go the cloud I think there's aspects and solutions that work very well there but for the vast majority I still think that there's you know the your data center is not going away and you do want to have control over some of the many of the different facets with inside the operational confines so therefore we start to look at how do we can do the best of what cloud offers but on Prem and that's realistically where we start to see the stronger push for those customers who still want to manage their data locally as well as maybe even work around some of the restrictions that they might have around cost and complexity hiring you know the different types of skills skill sets that are required to bring you know applications purely cloud native it's still that larger part of that digital transformation that many organizations are going for going forward with and realistically I think they're taking a look at the pros and cons and we've been doing cloud long enough for people recognize that you know it's not perfect for everything and that there's certain things that we still want to keep inside our own data center so I mean realistically as we move forward that's that that better option when it comes to a modern architecture they can do it you know we can deliver and address a diverse set of performance requirements and allow the organization to continue to grow the model to the data you know based on the data that they're actually trying to leverage and that's really what flash Wood was built or it was built for a platform that can address small files or large files or high throughput high throughput low latency scale to petabytes in a single namespace in a single rack as we like to put it in there I mean we see customers that have put you know 150 flash blades into production as a single namespace it's significant for organizations that are making that drive towards modern data experience with modern analytics platforms pure and Vertica have delivered an experience that can address that to a wide range of customers that are implementing you know the verdict technology I'm interested in exploring the use case a little bit further you just sort of gave some parameters and some examples and some of the flexibility that you have in but take us through kind of what the discuss the customer discussions are like obviously you've got a big customer base you and Vertica that that's on prem that's the the the unique advantage of this but there are others it's not just the economics of the the granular scaling of compute and storage independently there are other aspects so to take us through that sort of a primary use case or use cases yeah you know I mean I can give you a couple customer examples and we have a large SAS analyst company which uses verdict on flash play to authenticate the quality of digital media in real time and you know then for them it makes a big difference is they're doing they're streaming and whatnot that they can they can fine tune and grandly control that so that's one aspect that that we get address we have a multi national car con company which uses verdict on flash blade to make thousands of decisions per second for autonomous vehicle decision-making trees that you know that's what really these new modern analytics platforms were built or there's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time the impact Ives especially when we start to look at and you know the current state of affairs with Kovac in the coronavirus you know those types of technologies are really going to help us kind of get love and and help lower and been you know bend that curve downward so you know there's all these different areas where we can address the goals and the achievements that we're trying to look bored with with real-time analytic decision making tools like Berta and you know realistically as we have these conversations with customers they're looking to get beyond the ability of just you know you know a data scientist or a data architect looking to just kind of drive in information we were talking about Hadoop earlier we're kind of going well beyond that now and I guess what I'm saying is that in the first phase of cloud it was all about infrastructure it was about you know spinning up you know compute and storage a little bit of networking in there seems like the the a next a new workload that's clearly emerging is you've got and it started with the cloud databases but then bringing in you know AI and machine learning tooling on top of that and then being able to really drive these new types of insights and it's really about taking data these bogs this bog of data that we've collected over the last 10 years a lot of that you know driven by Hadoop bringing machine intelligence into the equation scaling it with either cloud public cloud or bringing that cloud experience on prams scale you know across your organizations and across your partner network that really is a new emerging work load do you see that and maybe talk a little bit about you know what you're seeing with customers yeah I mean it really is we see several trends you know one of those is the ability to take a take this approach to move it out of the lab but into production you know especially when it comes to you know data science projects machine learning projects that traditionally start out as kind of small proofs of concept easy to spin up in the cloud but when a customer wants to scale and move towards a real you know it derived a significant value from that they do want to be able to control more characteristics right and we know machine learning you know needs to needs to learn from a massive amounts of data to provide accuracy there's just too much data to retrieve in the cloud for every training job at the same time predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking you know we see this the visualization of data analytics is traditionally deployed as being on a continuum with you know the things that we've been doing in the long you know in the past you know with data warehousing data lakes AI on the other end but but this way we're starting to manifest it in organizations that are looking towards you know getting more utility and better you know elasticity out of the data that they are working for so they're not looking to just build ups you know silos of bespoke AI environments they're looking to leverage you know a platform that can allow them to you know do a I for one thing machine learning for another leverage multiple protocols to access that data because the tools are so much different you know it is a growing diversity of of use cases that you can put on a single platform I think organizations are looking for as they try to scale these environments I think there's gonna be a big growth area in the coming years gay ball I wish we were in Boston together you would have painted your little corner of Boston Orange I know that you guys are sharing but I really appreciate you coming on the cube wall-to-wall coverage two days at the vertical Vertica virtual big data conference keep you right there but right back right after this short break [Music]

hi everybody and welcome to this cube special presentation of the verdict of virtual Big Data conference the cube is running in parallel with day 1 and day 2 of the verdict big data event by the way the cube has been at every single big data event and it's our pleasure to be here in the virtual / digital event as well Gabriel Chapman is here is the director of flash blade product solutions marketing at pure storage gave great to see you thanks for coming on great to see you - how's it going it's going very well I mean I wish we were meeting in Boston at the Encore Hotel but you know and and hopefully we'll be able to meet it accelerate at some point you cheer or one of the the sub shows that you guys are doing the regional shows but because we've been covering that show as well but I really want to get into it and the last accelerate September 2019 pure and Vertica announced a partnership I remember a joint being ran up to me and said hey you got to check this out the separation of Butte and storage by a Eon mode now available on flash played so and and I believe still the only company that can support that separation and independent scaling both on permit in the cloud so Gabe I want to ask you what were the trends in analytical database and cloud that led to this partnership you know realistically I think what we're seeing is that there's been in kind of a larger shift when it comes to modern analytics platforms towards moving away from the the traditional you know Hadoop type architecture where we were doing on and leveraging a lot of direct attached storage primarily because of the limitations of how that solution was architected when we start to look at the larger trends towards you know how organizations want to do this type of work on premises they're looking at solutions that allow them to scale the compute storage pieces independently and therefore you know the flash play platform ended up being a great solution to support Vertica in their transition to Eon mode leveraging is essentially as an s3 object store okay so let's let's circle back on that you guys in your in your announcement of a flash blade you make the claim that flash blade is the industry's most advanced file and object storage platform ever that's a bold statement so defend that it's supposed to yeah III like to go beyond that and just say you know so we've really kind of looked at this from a standpoint of you know as as we've developed flash blade as a platform and keep in mind it's been a product that's been around for over three years now and has you know it's been very successful for pure storage the reality is is that fast file and fast object as a combined storage platform is a direction that many organizations are looking to go and we believe that we're a leader in that fast object of best file storage place in realistically would we start to see more organizations start to look at building solutions that leverage cloud storage characteristics but doing so on prem or multitude different reasons we've built a platform that really addresses a lot of those needs around simplicity around you know making things assure that you know vast matters for us simple is smart we can provide you know cloud integrations across the spectrum and you know there's a subscription model that fits into that as well we fall that that falls into our umbrella of what we consider the modern data experience and it's something that we've built into the entire pure portfolio okay so I want to get into the architecture a little bit of Flash blade and then better understand the fit for analytic databases generally but specifically Vertica so it is a blade so you got compute and a network included it's a key value store based system so you're talking about scale out unlike unlike viewers sort of you know initial products which were scale up and so I want to under in as a fabric base system I want to understand what that all mean so take us through the architecture you know some of the quote-unquote firsts that you guys talk about so let's start with sort of the blade aspect yeah the blade aspect meaning we call it a flash blade because if you look at the actual platform you have a primarily a chassis with built in networking components right so there's a fabric interconnect with inside the platform that connects to each one of the individual blades the individual blades have their own compute that drives basically a pure storage flash components inside it's not like we're just taking SSDs and plugging them into a system and like you would with the traditional commodity off-the-shelf hardware design this is a very much an engineered solution that is built towards the characteristics that we believe were important with fast file and fast object scalability you know massive parallelization when it comes to performance and the ability to really kind of grow and scale from essentially seven blades right now to a hundred and fifty that's that's the kind of scale that customers are looking for especially as we start to address these larger analytic spools they have multi petabyte datasets you know that single addressable object space and you know file performance that is beyond what most of your traditional scale-up storage platforms are able to deliver yes I interviewed cause last September and accelerate and and Christopher's been you know attacked by some of the competitors is not having a scale out I asked him his thoughts on that he said well first of all our Flash blade is scale-out and he said look anything that that that adds the complexity you know we avoid but for the workloads that are associated with Flash blade scale-out is the right sort of approach maybe you could talk about why that is well you know realistically I think you know that that approach is better when we're starting to learn to work with large unstructured data sets I mean flash plays uniquely architected to allow customers to achieve you know a superior resource utilization for compute and storage well at the same time you know reducing significantly the complexity that is arisen around these kind of bespoke or siloed nature of big data and analytic solutions I mean we really kind of look at this from a standpoint of you have built and delivered or created applications in the public cloud space that address you know object storage and and unstructured data and and for some organizations the importance is bringing that on Prem I mean we do seek repatriation that coming on on for a lot of organizations as these data egress charges continue to expand and grow and then organizations that want even higher performance in the what we're able to get into the public cloud space they are bringing that data back on Prem they are looking at from a standpoint we still want to be able to scale the way we scale on the cloud we still want to operate the same way we operate in the cloud but we want to do it within control of our own you know our own borders and so that's you know that's one of the bigger pieces to that is we start to look at how do we address cloud characteristics and dynamics and consumption metrics or models as well as the benefits and efficiencies of scale that they're able to afford but allowing customers that do that with inside their own data center yes are you talking about the trends earlier you had these cloud native databases that allowed the scaling of compute and storage independently of Vertica comes in with eon of a lot of times we talk about these these partnerships as Barney deals of you know I love you you love me here's a press release and then we go on or they're just straight you know go to market are there other aspects of this partnership that are that are non Barney deal like in other words any specific you know engineering you know other go to market programs can you talk about that a little bit yeah it's it's it's more than just you know I then what we consider a channel meet in the middle or you know that Barney type of deal it's the realistically you know we've done some first with Vertica that I think are really important if they think you look at the architecture and how we do have we've brought this to market together we have solutions teams in the back end who are you know subject matter experts in this space if you talk to joy and the people from vertigo they're very high on or very excited about the partnership because it often it opens up a new set of opportunities for their customers to to leverage Eon mode and you know get into some of the the nuanced aspects of how they leverage the depot for Depot with inside each individual compute node and adjustments with inside there I reach additional performance gains for customers on Prem and at the same time for them there's still the ability to go into that cloud model if they wish to and so I think a lot of it is around how do we partner as two companies how do we do a joint selling motions you know how do we show up and and you know do white papers and all of the the traditional marketing aspects that we bring devote to the market and then you know joint selling opportunities as exists where they are and so that's realistically I think like any other organization that's going to market with a partner or an ISP that they have a strong partnership with you'll continue to see us you know talking about our chose mutually beneficial relationships and the solutions that we're bringing to the market okay you know of course he used to be a Gartner analyst and you go over to the vendor side now but as but as it but as a gardener analyst you're obviously objective you see it all you know well there's a lot of ways to skin a cat there are there are there are strengths weaknesses opportunities threats etc for every vendor so you have you have Vertica who's got a very mature stack and and talking to a number of the customers out there we're using Eon mode you know there's certain workloads where these cloud native databases make sense it's not just the economics of scaling compute and storage independently I want to talk more about that there's flexibility aspects as well but Vertica really you know has to play its trump card which is look we've got a big on-premise state and we're gonna bring that you know Eon capability both on Prem and we're embracing the cloud now they're obviously you have to they had to play catch-up in the cloud but at the same time they've got a much more mature stack than a lot of these other you know cloud native databases that might have just started a couple of years ago so you know so there's trade-offs that customers have to make how do you sort through that where do you see the interest in this and and and what's the sweet spot for this partnership you know we've been really excited to build the partnership with Vertica and we're providing you know we're really proud to provide pretty much the only on Prem storage platform that's validated with the vertical yawn mode to deliver a modern data experience for our customers together you know it's it's that partnership that allows us to go into customers that on Prem space where I think that they're still you know not to say that not everybody wants to go the cloud I think there's aspects and solutions that work very well there but for the vast majority I still think that there's you know the your data center is not going away and you do want to have control over some of the many of the different facets with inside the operational confines so therefore we start to look at how do we can do the best of what cloud offers but on Prem and that's realistically where we start to see the stronger push for those customers who still want to manage their data locally as well as maybe even work around some of the restrictions that they might have around cost and complexity hiring you know the different types of skills skill sets that are required to bring you know applications purely cloud native it's still that larger part of that digital transformation that many organizations are going for going forward with and realistically I think they're taking a look at the pros and cons and we've been doing cloud long enough for people recognize that you know it's not perfect for everything and that there's certain things that we still want to keep inside our own data center so I mean realistically as we move forward that's that that better option when it comes to a modern architecture they can do it you know we can deliver and address a diverse set of performance requirements and allow the organization to continue to grow the model to the data you know based on the data that they're actually trying to leverage and that's really what flash Wood was built or it was built for a platform that can address small files or large files or high throughput high throughput low latency scale to petabytes in a single namespace in a single rack as we like to put it in there I mean we see customers that have put you know 150 flash blades into production as a single namespace it's significant for organizations that are making that drive towards modern data experience with modern analytics platforms pure and Vertica have delivered an experience that can address that to a wide range of customers that are implementing you know the verdict technology I'm interested in exploring the use case a little bit further you just sort of gave some parameters and some examples and some of the flexibility that you have in but take us through kind of what the discuss the customer discussions are like obviously you've got a big customer base you and Vertica that that's on prem that's the the the unique advantage of this but there are others it's not just the economics of the the granular scaling of compute and storage independently there are other aspects so to take us through that sort of a primary use case or use cases yeah you know I mean I can give you a cup of customer examples and we have a large SAS analyst company which uses verdict on flash play to authenticate the quality of digital media in real time and you know then for them it makes a big difference is they're doing they're streaming and whatnot that they can they can fine tune and grandly control that so that's one aspect that we get address we have a multi national car con company which uses verdict on flash blade to make thousands of decisions per second for autonomous vehicle decision-making trees that you know that's what really these new modern analytics platforms were built or there's another healthcare organization that uses Vertica on flash blade to enable healthcare providers to make decisions in real time the impact Ives especially when we start to look at and you know the current state of affairs with Kovac in the coronavirus you know those types of technologies are really going to help us kind of get love and and help lower and been you know bend that curve downward so you know there's all these different areas where we can address the goals and the achievements that we're trying to look bored with with real-time analytic decision making tools like Berta and you know realistically as we have these conversations with customers they're looking to get beyond the ability of just you know you know a data scientist or a data architect looking to just kind of drive in information we were talking about Hadoop earlier we're kind of going well beyond that now and I guess what I'm saying is that in the first phase of cloud it was all about infrastructure it was about you know spinning up you know compute and storage a little bit of networking in there seems like the the a next a new workload that's clearly emerging is you've got and it started with the cloud databases but then bringing in you know AI and machine learning tooling on top of that and then being able to really drive these new types of insights and it's really about taking data these bogs this bog of data that we've collected over the last 10 years a lot of that you know driven by Hadoop bringing machine intelligence into the equation scaling it with either cloud public cloud or bringing that cloud experience on prams scale you know across your organizations and across your partner network that really is a new emerging work load do you see that and maybe talk a little bit about you know what you're seeing with customers yeah I mean it really is we see several trends you know one of those is the ability to take a take this approach to move it out of the lab but into production you know especially when it comes to you know data science projects machine learning projects that traditionally start out as kind of small proofs of concept easy to spin up in the cloud but when a customer wants to scale and move towards a real you know it derived a significant value from that they do want to be able to control more characteristics right and we know machine learning you know needs to needs to learn from a massive amounts of data to provide accuracy there's just too much data to retrieve in the cloud for every training job at the same time predictive analytics without accuracy is not going to deliver the business advantage of what everyone is seeking you know we see this the visualization of data analytics is traditionally deployed as being on a continuum with you know the things that we've been doing in the long you know in the past you know with data warehousing data lakes AI on the other end but but this way we're starting to manifest it in organizations that are looking towards you know getting more utility and better you know elasticity out of the data that they are working for so they're not looking to just build ups you know silos of bespoke AI environments they're looking to leverage you know a platform that can allow them to you know do a I for one thing machine learning for another leverage multiple protocols to access that data because the tools are so much different you know it is a growing diversity of of use cases that you can put on a single platform I think organizations are looking for as they try to scale these environments I think there's gonna be a big growth area in the coming years gay ball I wish we were in Boston together you would have painted your little corner of Boston Orange I know that you guys are sharing but I really appreciate you coming on the cube wall-to-wall coverage two days at the vertical Vertica virtual big data conference keep you right there but right back right after this short break [Music]

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)