 Hey everyone, welcome to Las Vegas. TheCube is here live at the Venetian Expo Center for AWS re-invent 2022 amazing attendance. This is day one of our coverage. Lisa Martin here with Dave Vellante. David, it's great to see so many people back. We've been having great conversations already. We have wall-to-wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real-time data and real-time analytics no longer are nice to have. That is a differentiator and a competitive advantage. It's all about the data. I mean, you know, I love the topic and it's got so many dimensions and such texture. Can't get enough of data. I know. We have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to theCUBE. Thanks for having me. It's great to be here. So here we are day one. I was telling you before we went live we're nice and fresh hosts. Talk to us about what's new at Influx Data since the last time we saw you at re-invent. That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. It does really feel like, I know there was a show last year, but this feels like the first post-COVID shows. A lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead-in to big data. I think, you know, if we were to talk about big data five, six years ago, what would we be talking about? We've been talking about Hadoop. We were talking about Clodera. We were talking about Hortonworks. We were talking about big data lakes, data stores. I think what's happened is, is this interesting dynamic of, let's call it, if you will, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data. You've got this observability data. You've got graph data. You've got document data. And what you're seeing in the market, and now you have time series data, what you're seeing in the market is, this incredible capability by developers, as well, and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hadoop, or Oracle, or Postgres, or MySQL, but in fact represent different data types. So for us, what we care about is time series. We care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Because when you think about what drives AI, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is, we've developed this new open source engine called IOX. And so it's basically a refresh of the whole database, a columnar database that uses Apache Arrow, Parquet, and DataFusion, and turns it into a super powerful real-time analytics platform. It was already pretty real-time before, but it's increasingly now. And it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger, but faster, faster data. That's primarily what we're talking about at the show. So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your customer opportunities? I think it's really an interesting market in that you've got all of these different approaches to database, whether you take data warehouses from Snowflake, or arguably Databricks also, and you take these individual database companies like Mongo, Influx, Neo4j, Elastic, and people like that. I think the commonality you see across the world is many of them, if not all of them are based on some sort of open source dynamic. So I think that is an intractable trend. That will continue for on. But in terms of the broader database market, our total available TAM, lots of these things are coming together in interesting ways. And so the way that we want to ride, because it's all big data, it's all increasingly fast data, and it's all machine learning, and AI is really around that measurement issue, that instrumentation. The idea that if you're going to build any sophisticated system, it starts with instrumentation, and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how? I have to follow, quick follow up. Why did you say arguably Databricks? I mean, open source ethos? Well, I was saying arguably Databricks, because I mean, it's a great company, and it's based on Spark, but there's quite a gap between Spark and what Databricks is today, and in some ways, Databricks from the outside looking in looks a lot like Snowflake to me, looks a lot like a really sophisticated data warehouse with a lot of post-processing capability. And with an open source. Less than a poor database, yeah. Right, right, yeah, I totally agree. Okay, thank you for that. Not arguably like they're not a good company. No, no, they got great momentum, and I just curious, you know, so. So talk a little bit about IOX, and what it is enabling you guys to achieve from a competitive advantage perspective, the key differentiators, give us that scoop. So if you think about, so our old storage engine was called TSM, also open sourced, right, and IOX is open sourced, and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics, and handling those at scale, and making it super easy for developers to use. But our old data engine only supported either a custom graphical UI that you'd build yourself on top of it, or a dashboarding tool like Grafano or Chronograph or things like that. With IOX, two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft BI, and so you're taking that same data that was available for instrumentation, and now you're using it for business intelligence also. So that became super important, and it kind of answers your question about the expanded market, expands the market. The second thing is when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that it's a multiplication of measurements in a table, and so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed eye access to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better, and so it's pretty exciting. So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? At scale. Between the same database, but different measurements very quickly. Different tables. Yeah, yeah, yeah. So you can handle, so you could say, I want to look at the way the way the noise levels are performed in this room, according to 400 different locations on 25 different days over seven months of the year, and each one is a measurement, each one adds to cardinality, and you can say, I want to search on Tuesdays in December what the noise level is at 221 p.m., and you get a very quick response. That kind of instrumentation is critical to a smarter system. How are you able to process that data at a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? It's a columnar database. It's built on Parquet and Apache Arrow, but to spice to say, without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. So it's that purpose-built aspect of it? It's the purpose-built aspect. You couldn't take Postgres and do the same thing. Right, because a lot of vendors say, oh yeah, we have time series now. Yeah, yeah, right. And they do, but it's not, the founding of the company came because Paul Dix was working on Wall Street building time series databases on HBase, on MySQL, on other platforms and realized every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are downing hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, when you think about all those data points, talking about an ingest level that just doesn't, you know, just databases aren't designed for that. And so it's not just us. Our competitors also build good time series databases and so the category is really emergent. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOX. Yeah, sure. And I love this, I love this story because, you know, Tesla may not be in favor because of the latest Elon Musk rates, but, but, so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, the stuff, seeing the charging on your car is all captured in Influx databases that are reporting from power walls and mega power packs all over the world and they report to a central place at Tesla's headquarters and they report out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this data all the time. So it's a great customer story. And actually if you go on our website you can see I did an hour interview with the engineer that designed the system because the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability. Right, yeah. Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOX, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? Well, so it's relatively new. It just arrived in our cloud product so Tesla's not using it today. We have our first set of customers starting to use it. We, it's in open source so it's a very popular project in the open source world. But the key issues are really the stuff that we've kind of covered here which is that a broad SQL environment so accessing all those SQL developers, the same people who code against Snowflakes data warehouse or Databricks or Postgres can now can code that data against influx, open up the BI market, it's the cardinality, it's the performance, it's really an architecture. We've been doing this for six years. It's the next generation of everything we've seen, how you make time series be super performant. And that's only relevant because more and more things are becoming real time. As we develop smarter and smarter systems, the journey is pretty clear. You instrument a system, you let it run, you watch for anomalies, you correct those anomalies, you re-instrument the system, you do that four billion times you have a self-driving car, you do that 55 times you have a better podcast that is handling its audio better. So everything is on that journey of getting smarter and smarter. So you guys, you guys are big committers to IOX, right? Yes. Talk about how you support the development, the surrounding developer community, how you get that flywheel effect going. First, I mean, it's actually a really kind of, let's call it it's more art than science. First of all, you come up with an architecture that really resonates for developers. And Paul Dixar founder really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parquet, which is the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said, what this thing needs is a columnar database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing an IOX in GitHub commit and slowly but over time in hacker news and other people go, oh yeah, this is fundamentally right. It addresses the problems that people have with things like click house or plain databases or close and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become- The core. Yeah. And you build out from it and so super and so we chose to have an MIT open source license. It's not some secondary license. Competitors can use it and competitors can use it against us. Yeah, that's very cool. One of the things I know that influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to awesome for a developer? It comes from that original story where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem. It should be able to happen very quickly. So it's got a schema list background, so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper. And pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our DNA, so we do pretty well, but I always feel like we can do better. So if you were to put a bumper sticker on one of your Teslas about Influx data, what would it say? By the way, I'm not rich. It just happened to be that we have two Teslas. And we haven't for a while, we're just committed to that. So ask the question again, sorry. Bumper sticker on Influx data, what would it say? How would I understand? It'd be time to awesome. It'd be that phrase, it's time to awesome, right? Love that. Yeah, I love it. Time to awesome. Evan, thank you so much for joining Dave and me on the program. Thank you guys, it's been really fun. Great to have you on, Evan. Great to have you back, talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. That's great. Thank you. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.