(intense orchestral music) >> Hey 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 'em on theCUBE before, it's Evan Kaplan, he's the CEO of InluxData. Evan, great to see you. >> Yeah, hey thanks for having me. >> Absolutely. So for people that aren't familiar with the company, give 'em kind of the 101 on Influx. >> Yeah so, InfluxData is an opensource platform for collecting metrics and events at scale. The company is about almost four years old, has a large selection of tier one customers, is broadly accepted by developers as the number one time-series platform out there, so. >> So a lot of people talk about collecting data, 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, to put this is even in the broader context of what we're looking at is the 20 year break-up of the Oracle, DB2 and Formex franchise that dominated and relational databases were the answer to 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 also, really interesting space. I think our innovation has been in 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 verbose 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 'em 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 performant 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 a fundamentally time-series based database. As opposed to Splunk or Elastic or, which are primarily search-based 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, such a great question. So, the fundamental platform is called the TICK Stack, and it stands for Telegraf 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 micro-service 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 Chronograf 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 on time-series oriented graphing, and then there's Kapacitor which takes action on the data. What we don't do is the super high sophisticated analytics. There are 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. >> Right. So when the Kapacitor, does your application then take action on those things? >> Yes. Yeah, so, it'd do everything from alerting, to sending out another machine request, to spinning up a new Kubernetes pod, to basically scaling the application, 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? >> Yes, yeah. so you're in between? >> And usually, we're instrumented the way a 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 Telegraf, and 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, it's got a tolerance you can self adjust for. So it's the beginning of kind of the self-healing system. >> Okay, and I know that Telegraf is definitely opensource, are the other three? >> All four are open-source All four are open-source. >> Everything, in our world, everything for a developer is free, 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, where we make money is, our closed source clustering, sharding, distributing the database, if you decide you want to run highly available in the production environment, you would buy our closed-source stuff. We have about 430 customers who run our closed source stuff on top of the opensource. >> So, it is kind of like a MapR to Hadoop if you will, where, you know, it's built on, built on the opensource, and then they've got their proprietary stuff kind of wrapped around it, almost like an open core? Or is that a? >> Yeah, it's a little It's a little different than the normal Hadoop stuff. One is, our stuff doesn't have any external dependencies. It can work with other third party projects, but just, it's a platform onto itself, there aren't 25 projects. There are four different projects, we own them all, they come across as a single binary, and it's not part of Apache. >> So they're integrated So the TICK is the full TICK >> Yes, and then you put the clustering on top. So there's some similarity, but not being part of Apache, we can control and keep clean what that experience is. And we're about, the thing that's been most successful for us is, well Paul our founder who is my partner, it's called time to awesome, 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 opensource. >> So it's interesting, the giant opportunity is really around IoT, just in terms of the explosion of the sensor data, and we see that coming, and we were at AT&T show a couple weeks ago, talking about 5G which is, slowly, slowly coming down the road, (Evan laughs) they've 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've got real problems. So you said you're seeing, your main success now, your go to market application, is around application monitoring? Would that be accurate, or what is kind of your? >> Yeah, there are two broad things, and they're both very similar technology as a service. One is the central monitoring stuff so, Tesla's Power Wall, Seimens' Windmills, a variety of solar companies build Telegraf into their platforms and then use InluxData 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 that's the idea is it's a general purpose platform for collecting and instrumenting instrumenting the applications or the sensors, either one, or both. >> Okay, and so what are you guys working on now, what's next, kind of raise the profile, get some new stuff >> Yeah, so we are-- before the whole IoT thing completely explodes, we're not quite there yet but it's coming down the pike. >> 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 super set of the log market, it should be. As you think about just the instrumentation of the physical world, how much instrumentation is going on, your clothes, your cars, your homes, your industrial devices, my 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 opensource, that a bunch of other people can use with their data stores. We're rolling out a new API-driven service, so that people can store these things directly in the could natively, so all they have to do is know our API. So we're really trying to push from the technology limit we're a product-driven company, and so, and an opensource-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, you're barely starting to see it, 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 an 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 out 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 has not really been >> That's pretty insightful. >> opened up very wide. Yeah, no it's just starting to open up, and that whole notion of product telemetry, my prediction is is that, as development teams grow and things like that, you're going to have telemetry experts, people are going to be specializing. 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 'til you instrument, whether it's telemetry from a product, it's the engagement or this. So 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, apriori, 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, we can see this with self-driving cars, 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 this stuff, and then how do you separate signal from noise and how do you know what to do? So you've got to automate a whole bunch of this. >> And you know if we had an aspiration it would be we're not going to write the applications that 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. Time-stamped data, for lack of a better way to say it. >> It sounds like you're in a pretty good space, Evan. >> Pretty excited (chuckles), thank you. Thanks for saying that, but yeah, we're pretty excited. >> Alright, well thanks for taking a few minutes out of your day and sharing the story, we look forward to watching the journey. >> Yeah. Thanks man. Alright, take care. >> Alright, 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. (intense orchestral music)