 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, new casino, but obviously Coronavirus has changed all that, our hearts go out and our empathy to those people 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? Yeah, you know, I don't know, so I've been dealing with machine data a long time, so for those of you who don't know what that is, you know, you can imagine servers or whatever goes on in a data center or in a SaaS 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 out. And so, you know, I built this, basically spent my career building that sort of thing. So, you know, 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, you know, going in and understanding the data and, you know, extracting the data and all that stuff. It's very time consuming. If you've done it like five times, you don't wanna do it again. So really, my idea was, you know, at this point with machine learning where it's at, there's gotta be a better way, so. 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's really all about bringing that, bringing that value to the market. That's cool, I wanna get into that. Just better understand kind of 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. A lot of tech chops. Yeah. Give us some color there. Yeah, I mean, so, you know, I started in the valley, I guess, 20 years ago when my son was born, I left grad school. I was in grad school over at Berkeley, biophysics, and I realized I need to go get a job. So I ended up starting in software and I've been there ever since. I mean, spent a lot of time at sort of, I guess I cut my teeth at NetApp, which was a storage company. And then I've co-founded a business called Blasping, 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 Blasping. So I went over there and after Nimble though, which where I built the InfoSight platform, that's where I kind of, you know, 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 Zebrian. So that's really my career in a nutshell. And you got Hello Kitty over your right shoulder, which is kinda cool. That's right. And then up to the left, you got your monitor, right? Well, I had it, it's over here yet. Okay, 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? That's right, that's right. So Hello Kitty's over here, so I have a daughter and she, you know, set up 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 in and 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 on. You guys are yelling at me. But so obviously we're talking to somebody with serious coding chops. And I also had the Nimble Info site. I think it was one of the best pickups that 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 Info site and port 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 buzzwords, but the proof is in their ability to bring that IP to other parts of the portfolio. So well done. Yeah, well thanks, I appreciate that. I mean, you know, they've got a fantastic team there. And the other thing that helps is, you know, 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 use Vertica there for that. And then you build on top of that. That just taking the time to build that layer is what lets you build a scalable sort of platform. Yeah, so, you know, why Vertica? I mean, Vertica's been around for a while. You remember you had the, you had the old RDBMS, you know, Oracle's DB2 SQL server. And then the database was kind of a boring market. And then all of a sudden you had the, all these MPP companies came out a spate of them. They all got acquired, including Vertica. And they've all sort of, you know, disappeared and, you know, morphed into different brands and Micro Focus has preserved the Vertica brand. And 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 one to build what I would call a real column store that's kind of market capable, right? So there was the C-Sore 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, you know, let's lay things out in a columnar way. And then 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 sort of, you know, it takes full advantage of things like run length encoding, delt file encoding and block dig compression. And so you end up with these massive orders of magnitude savings in terms of the data that's being pulled off 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, 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 know, your startup, and so a lot of startups would say, okay, we're going with a born in the cloud database. Vertica touts that, well, look, we've embraced cloud. You know, we have, we run in the cloud, we run on-prem, you know, 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, you know, able to, I mean, obviously that's why you chose him, but I'm interested from a technologist standpoint as to, you know, why you again made that choice given all these other choices around there. Right, I mean, again, I'm not, so as I explained to column store, which I think is the appropriate definition, I'm not aware of another cloud native, so I'm not aware of other cloud native transactional databases. I'm not aware of one that has the analytics performance and I've tried some of them. So it's 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 the reliability of Eon. It's interesting, I've been saying for years that Vertica is a diamond in the rough and its 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 there's obviously putting some investments in there. 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 gonna go after? Add some color to that. Yeah, so our broader market is monitoring software. That's the 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 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 MTTR is really what people need to focus on to keep their customers happy. If you're a SaaS company, MTTR is gonna 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. Nowadays applications are 10 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 set up automation for it. But the ones that really cost a lot of time in terms of service disruption are unknown unknowns. And now you gotta go dig into this massive mass of data. So observability is about making tools to help you do that, but it's still gonna take you hours. And so our contention is you need to automate the eyeball. That the bottleneck is now the eyeball. And so you have to get away from this notion of a person's gonna be able to do it infinitely more efficiently and recognize that you need automated help. When you get an alert, it shouldn't be that, hey, something weird's happening, now go dig in, it should be, here's a root cause and the 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 Zebraim does. Yeah, that's awesome. And you're right. Last thing you want is just another alert and say go figure something out cause there's a problem. So how does it work, Larry? In terms of what you built there, could 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 file. Metrics, there's actually sort of a framework that's really popular in sort of DevOps 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, like 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 gonna have exporters that export all their stats 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 wanna pull all those back and you wanna be able to associate sort of, okay, 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. We say, let's structure those into tables, right? So where I used to have a sort of 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 type's gonna occur again and it'll say X happened six times or X happened three times. So if I see that as a human being, right? I can say, oh, clearly that's the same thing. And what's interesting here is the times that X happened and that this number, right? I may wanna 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 a 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 type structured that way, everything 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 for those apps or how do you handle that? Yeah, that's a really good question. So you're right. So if I go and install a typical sort of 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'll be things like a timestamp 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 sort of indexed for search. And so our approach is we actually go in with machine learning, we structure that whole thing. So there's a table and it's gonna have a column called severity and timestamp and function name and then it's gonna have columns that correspond to the parameters that are in that event. And it'll have a name associated with sort of 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 homegrown app that has no collectors or no parsers defined or anything. It'll work immediately just as well as it would work on anything else. And that's important, right? Because you can't be asking people for connectors to their own applications. It's just, it becomes now they've got 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 monitor for you. That's awesome. I mean, that is really a breakthrough innovation. So nice, love to see that hit in the market. Who do you sell to? Both types of companies and what role within the company? Oh, well, definitely there's two main sort of pushes that we've seen. I should say pulls. One is from sort of 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, cause when we find an incident in a staging environment, yeah, half the time it's cause, you know, 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. Like you have engineering users and I wouldn't, they're not, they don't have titles like QA. They're dev engineers or dev managers that are really interested. And then you've got sort of DevOps and SRE people that are interested. Go ahead and how do I consume your product? That's just the SaaS I sign up and you said within three minutes I'm up and running and I'm paying by the drill. 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, so, you know, Docker and containers and all that. So now there's, you know, container orchestrators have become, I wouldn't say ubiquitous, but they're very popular now. So it's kind of, it's kind of going, it's kind of on that inflection curve. I'm not exactly sure that penetration, but I'm going to say 30% probably of shops that we're interested are using container orchestrators. So if you're using Kubernetes, basically you can install our Kubernetes chart, which basically means copying and pasting the 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 log in 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 the 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 in, but you don't have to. You shouldn't have to, right? You should never have to do any more work. That's great. And as I say, so it's a SaaS 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 as far as I know, it's as cheap or cheaper than any other log manager or sort of metrics product. It's in that same neighborhood as the very low priced ones because right now we're not like, 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, AWS? Yeah, that's right. Yeah, that's right. Oh, I should have also pointed out, you can have a free account. If it's less than some number of gigabytes a day, we're not going to charge you. 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 biz dev guy, but what I'll say is that when you, if you're doing something that ends 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 test and fraud and paid and all that. I mean, you know what, just call them up. Yeah, and that's why I brought it up. It's a Vertica, they have a Community Edition which is not neutered. It runs Eon, it just has limits on clusters and storage, but yeah. So to your point, we want a multi-tenant, right? So it's big just because it's multi-tenant. We have hundreds of users on that. 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. We know what, so the important thing, so here's what's important. What's important is that I don't have to worry about that layer of our stack. Like 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 have 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 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. Yeah, we 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 and you got the product market fit down, you're ready to rock, right? Yeah, that's right. So I mean, so we're doing a launch probably, well, when this airs, it'll probably be the day before this airs. So basically, yeah, I mean, so we've finally, we've got people, we've got like literally in the last, I'd say six to eight weeks, it's just been this sort of, you know, peak 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. Great, 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, 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.