 And welcome back everybody, Jeff Frick here with theCUBE. We are taking a short break from the madness of the conference season to do some CUBE conversations here in the Palo Alto studio, which we always like to do and meet new people and hear new stories, learn about new companies. And today we've got a new company, we've never had them on theCUBE before, it's Evan Kaplan, he's the CEO of Influx Data. Evan, great to see you. Yeah, hey, thanks for having me. Absolutely, so for people that aren't familiar with the company, give them kind of the 101 on Influx. Yeah, so Influx Data is an open source platform for collecting metrics and events at scale. Company is about almost four years old, has a large selection of tier one customers, is broadly accepted by developers, is the number one time series platform out there. So great. So a lot of people talk about collecting data, right? So we've been doing Splunk since 2012 and they really found something interesting on log files and took it a whole nother level. So there's a lot of people that are capturing events. So what do you guys do that's a little bit different? How are you slicing and dicing this opportunity? Yeah, I mean to put this even in the broader context of what we're looking at is the 20 year breakup of the Oracle DB2 InformX franchise that dominated the relational databases where they answered all problems. And so if you look at a company like Splunk working on logs, they optimized a platform for those logs, for that data set, Elastic. It's also a really interesting space. I think our innovation has been is saying, hey, where the world's going, where all of these complex systems are going, particularly IoT, is to real time view of the data. And so rather than collect for both logs, historical views of the data and things like that, real system operators, real developers and builders want to instrument their applications, their infrastructure so you can view them in real time. The place where the rubber hits the road is IoT. Sensors spit out metrics and events, period, full stop. And so if you want to be performing in how you handle your instrumentation of the physical world and how you do your machine learning and how you want to manage these systems, you use fundamentally a time series based database as opposed to Splunk or Elastic or which are primary search databases. And are you primarily capturing and standardizing the data to feed other analytics tools or do you have the whole suite where you're doing some of the analytics as well? Yeah, so that's a great question. So the fundamental platform is called the TIC stack and it stands for Telegraph, which is a collector which has about 200 different collectors that sit out there in the world and collect everything from SNMP data to Oracle data to application to microservice data to Kubernetes to that sort of stuff. There's Influx, which is the DB which is highly optimized for millions and millions of writes a second. So collecting data points and samples. There's Chronograph which is the visualization engine and so it allows you as soon as the data comes input you can see how it's graphed see it's time series oriented graphing and then there's Capacitor which takes action on the data. What we don't do is a super high sophisticated analytics or lots of companies in Silicon Valley who take our data, pump it up and then we put it back on the platform to build a control loop for it. So in the Capacitor, so then you, does your application then take action on those things? Yeah, so it do everything from alerting to sending out another machine request to spinning up a new Kubernetes pod to basically scaling applications, self-healing. Right. So does it fit in between a lot of those other types of applications that are sending off notifications and those types of things? Yeah, and usually we're instrumented the way the standard developer or an architect or CTO does is they look at a complex application or a complex set of sensors. They instrument with Influx and Telegraph. They collect that data, they view it in real time and then they build control loops, automation loops to make that easier. So when you see a problem that's out of tolerance, you can self-adjust for it. It's the beginning of a kind of a self-healing system. Okay, and I know the Telegraph is the open, it's definitely open source. Are the other three? All four are open source. All four are open source. Everything, in our world, everything for a developer is free. Okay. So and a single note of Influx can handle a couple million writes a second which is really, really performant to run in production. Where our business model is, is where we make money is our closed source clustering, sharding, distributing the database. If you decide you want to run it highly available in a production environment, you would buy our closed source stuff. We have about 430 customers who run our closed source stuff on top of the open source. So is it kind of like say a map R to Hadoop, if you will, where it's built on the open source and then they've got their proprietary stuff kind of wrapped around almost like an open core? It's a little bit different than the normal Hadoop stuff. One is our stuff doesn't have any external dependencies. It can work with other third party product but just it's a platform into itself. There aren't 25 projects. There are four different projects. We own them all. They come across as a single binary. Okay. And it's not part of a patchy. So they're integrated. So the tick is the full tick. Yeah, and then you put the clustering on top. So there's some similarity but not being part of a patchy, we can control and keep clean what that experience is. And we're about, the thing that's been most successful for us is what Paul, our founder, who's my partner, has called Time2Awesome, the idea that a developer in 10 minutes can very quickly be up and instrumenting an application or a set of sensors and see that data pouring in within 10 minutes from going to the site and downloading the open source. So it's interesting that the giant opportunity is really around IoT just in terms of the explosion of the sensor data and we see that coming and we're at AT&T show a couple of weeks ago talking about 5G, which is slowly coming down the road. They got the standards fixed. But in terms of the, you said the shorter term, nobody has budget, I always like to joke, nobody has budget for a new platform. They do have budget for new applications because they got real problems. So you said, you're seeing your main success now you're going to market application is around application monitoring, is that accurate or what is kind of your- Yeah, there are two broad things and they're both very similar technologies, but service. One is the sensor monitoring stuff. So Tesla's Powerwall, Siemens, windmills, a variety of solar companies build, telegraph into their platforms and then use influx data to collect and store that information and analyze it. On the software side, people like IBM's cloud service running their network and their fabric, SAP with Ariba, Cisco with all their collaboration stuff, they instrument their software applications. And so the idea is it's a general purpose platform for collecting and instrumenting the applications or the sensors, either one or both. And so what are you guys working on now? What's next? Got to raise the profile, get some new stuff before the IoT thing completely explodes. We're not quite there yet, but it's coming down to five. But we're starting to see it really happen. So that's really exciting for us. And this is just a really, really big market. It's certainly a superset of the log market. As you think about just the instrumentation of the physical world, how much instrumentation is going on, your clothes, your cars, your home, your industrial devices, you know. I watch how much sensor data there is. We think this is a tremendously large market. So we're doing a couple of things. One is we're about to introduce a new language for querying these kinds of time series data that's going to be open source that a bunch of other people can use with their data stores. We're rolling out a new API driven service so that people could store these things directly in the cloud natively. So all they have to do is know our API. And so we're really trying to push from the technology limit. We're a product driven company. And so an open source driven company. So we're trying to push that. That community is super important to us. It's so wild to me. The opportunity to have a closed feedback loop between someone's product back to the barn. And you're barely starting to see a Tesla obviously is a good example. They're slowly seeing it in other places. But what a fundamental change in manufacturing from building a product, making some assumptions about use, shipping that product to your distribution. And then maybe you get some feedback now and then versus actually monitoring the way that that thing is actually used by your end user. Whether it's a product like a car or even a software application as you're rolling at all these different apps and features in the apps. How are people using it? Are they using it? Where do you double down? Where do you back off? And that loop is not really opened up very wide. It's pretty insightful. Yeah, no, it's just starting to open up. And then that whole notion of product telemetry. My prediction is that as development teams grow and things like that, you're going to have telemetry experts. People are going to be specialized in how do you instrument these products so you get maximum engagement and usage and things like that. So I think that's pretty insightful on your part. If you think about it from a systems point of view, right? Instrumentation is first. You can't do anything till you instrument whether it's telemetry from a product to see engagement or that sort of instrumentation is first. Visibility in real time is second. So observability is the big thought in systems application and building now. This notion of observing your system in real time because you don't know, you don't a priori it's impossible to know a complex system, how it's going to behave. Then it's automation, right? So like, okay, now I can see these behaviors. How do I automate something that makes the experience for you, the user better? But lastly, you know, we can see this with self-driving cars. It's, you know, it's autonomy. It's the idea that the system becomes self-healing and AI and those sorts of things. But that's kind of the last step. There's a lot of learning in that process to get there. And it has to be automated because at scale, there's no way for people to keep up with the stuff. And then how do you separate signal from noise and how do you know what to do? So you got to automate a whole bunch of it. And you know, and if we had an aspiration, it would be, we're not going to write the applications to do these things. But what we want to do is be that system of record so that people have a really efficient, effective metrics and events store so they can really track and keep track of all that engagement. And you know, timestamp data, for lack of a better way of saying it. Sounds like you're in a pretty good space, Evan. We're pretty excited. Thank you. Thanks for saying that. But yeah, we're pretty excited. All right, well, thanks for taking a few minutes out of your day and sharing the story. We look forward to watching the journey. Thanks, man. All right, take care. All right, thanks. He's Evan, I'm Jeff, you're watching theCUBE. We're having a CUBE conversation in Palo Alto. We'll see you next time. Thanks for watching.