 Okay, we're back with Tim Yochum who is the director of engineering at Influx Data. Tim, welcome, good to see you. Good to see you, thanks for having me. You're really welcome. Listen, we've been covering open source software on theCUBE for more than a decade and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on open source, mobile, social platforms, key databases and of course Influx DB and Influx data has been a big consumer and contributor of open source software. So my question to you is where have you seen the biggest bang for the buck from open source software? So yeah, Influx really, we thrive at the intersection of commercial services and open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product Influx DB. You know, but I got to ask you, Tim, because one of the challenges that we've seen in particular, you saw this in the heyday of Hadoop, the innovations come so fast and furious and as a software company, you got to place bets, you got to, you know, commit people and sometimes those bets can be risky and not pay off. So how have you managed this challenge? Oh, it moves fast. Yeah, that's a benefit though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often, we try a lot of things. You look at Kubernetes, for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day. So we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of been off the charts and seen the most significant adoption and velocity, particularly along with cloud, but really Kubernetes is just still up and to the right consistently, even with the macro headwinds and a lot of the stuff that we're sick of talking about. So what are you doing with Kubernetes in the platform? Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere at AWS Azure Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code. So our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure and Google and figure out how to deliver services on those three clouds with all of their differences. Just to follow up on that, is it? No, so I presume it sounds like there's a pass layer there to allow you guys to have a consistent experience across clouds and up to the edge, you know, wherever. Is that correct? Yeah, so we've basically built more or less platform engineering as is the new hot phrase. You know, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering influx cloud. Yeah, and I know I'm taking a little bit of attention but is that, I'll call it a pass layer if I can use that term. Is that other specific attributes to influx DB or is it kind of just generally off the shelf paths? You know, is there any purpose built capability there that is value add or is it pretty much generic? So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage cloud provider services for instance, Postgres databases for metadata perhaps. Get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on that has consistency that is all generated from code that we can as an SRE group, as an ops team that we can manage with very few people really and we can stamp out clusters across multiple regions in no time. So sometimes you build, sometimes you buy. How do you make those decisions and what does that mean for the platform and for customers? Yeah, so what we're doing is it's like everybody else will do. We're looking for trade offs that make sense. We really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team and of course for customers, you don't even see that but we don't want to try to reinvent the wheel. Like I mentioned with SQL data stores for metadata perhaps let's build on top of what these three large cloud providers have already perfected and we can then focus on our platform engineering and we can have our developers then focus on the influx data software, the influx cloud software. So take it to the customer level. What does it mean for them? What's the value that they're going to get out of all these innovations that we've been talking about today and what can they expect in the future? So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine on a single server or what have you but then you want to scale up. We have some 270 terabytes of data across over four billion series keys that people have stored. So there's a proven ability to scale. Now in terms of the open source software and how we've developed the platform you're getting highly available high cardinality time series platform. We manage it and really as I mentioned earlier we can keep up with the state of the art. We keep reinventing. We keep deploying things in real time. We deploy to our platform every day repeatedly all the time and it's that continuous deployment that allows us to continue testing things in flight rolling things out that change new features better ways of doing deployments safer ways of doing deployments. All of that happens behind the scenes and we had mentioned earlier Kubernetes. I mean, that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the influx cloud platform you really are able to take advantage of new features immediately. We roll things out every day and as those things go into production you have the ability to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure. You know, let us do that for you. So that makes sense. So is the innovations that we're talking about in the evolution of influx DB do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both but is it opening up new territory for customers? Can you add some color to that? Yeah, it really is. It's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are really the hot thing. IoT, industrial IoT especially. People want to just shove tons of data out there and be able to do queries immediately and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their data store backbone and then they use edge computing with RLSS product to ingest data from say multiple production lines and down sample that data, send the rest of that data off to influx cloud where the heavy processing takes place. So really us being in all of the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the business trying to manage that big data, have us take care of that. And of course, as we change the platform and users benefit from that immediately. And so obviously you've taken away a lot of the heavy lifting for the infrastructure. Would you say the same thing about security, especially as you go out to IoT and the edge, how should we be thinking about the value that you bring from a security perspective? Yeah, we take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data we store is kept private. It's of course always a concern. You see in the news all the time, companies being compromised. That's something that you can have an entire team working on, which we do to make sure that the data that you have whether it's in transit, whether it's at rest is always kept secure, is only viewable by you. You look at things like software bill of materials. If you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, as we use new tools, that's something that's just part of our jobs to make sure that the platform that we're running it has fully vetted software. And with open source especially, that's a lot of work. And so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip than they used to. But that is really just part of the day in the life for folks like us that are building platforms. And that's key, I mean, especially when you start getting into the, you know, we talk about IoT and the operations technologies, the engineers running that infrastructure. You know, historically, as you know, Tim, they would air gap everything. That's how they kept it safe, but that's not feasible anymore. Everything's connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R&D so you can focus on some of the other activities. All right, give us the last word and the key takeaways from your perspective. Well, you know, from my perspective, I see it as a two lane approach with influx, with any time series data. You know, you've got a lot of stuff that you're going to run on-prem, what you mentioned, air gapping. Sure, there's plenty of need for that. But at the end of the day, people that don't want to run big data centers, people that want to entrust their data to a company that's got a full platform set up for them, that they can build on, send that data over to the cloud. The cloud is not going away. I think more hybrid approaches is where the future lives and that's what we're prepared for. Tim, I really appreciate you coming to the program. Great stuff, good to see you. Thanks very much, appreciate it. Okay, in a moment, I'll be back to wrap up today's session. You're watching theCUBE.