(upbeat music) >> Okay today, we welcome Evan Kaplan, CEO of InfluxData, the company behind InfluxDB. Welcome Evan, thanks for coming on. >> Hey John, thanks for having me. >> Great segment here on the InfluxDB story. What is the story? Take us through the history, why time series? What's the story? >> So the history history is actually pretty interesting. Paul Dix my partner in this and our founder, super passionate about developers and developer experience. And he had worked on wall street building a number of time series kind of platform, trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave. Which means you had to do a ton of work just to start doing work. Which means you had to write a bunch of extrinsic routines, you had to write a bunch of application handling on existing relational databases, in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view. This is not how developers should work. And so in 2013, he went through Y Combinator, and he built something for, he made his first commit to open source InfluxDB in the end of 2013. And he basically, you know from my point of view, he invented modern time series, which is you start with a purpose built time series platform to do these kind of workloads, and you get all the benefits of having something right out of the box. So a developer can be totally productive right away. >> And how many people are in the company? What's the history of employees is there? >> Yeah, I think we're, you know, I always forget the number but something like 230 or 240 people now. I joined the company in 2016, and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. 'Cause if you think about it, what sensors do is they speak time series. Pressure, temperature, volume, humidity, light, they're measuring, they're instrumenting something over time. And so I thought that would be super relevant over the long term, and I've not regretted it. >> Oh no, and it's interesting at that time if you go back in history, you know, the role of database. It's all relational database, the one database to rule the world. And then as cloud started coming in, you started to see more databases proliferate, types of databases. And time series in particular is interesting 'cause real time has become super valuable from an application standpoint. IOT which speaks time series, means something. It's like time matters >> Times yeah. >> And sometimes data's not worth it after the time, sometimes it's worth it. And then you get the data lake, so you have this whole new evolution. Is this the momentum? What's the momentum? I guess the question is what's the momentum behind it? >> You mean what's causing us to grow so fast? >> Yeah the time series, why is time series- >> And the category- >> Momentum, what's the bottom line? >> Well think about it, you think about it from a broad sort of frame which is, what everybody's trying to do is build increasingly intelligent systems. whether it's a self-driving car or a robotic system that does what you want to do, or a self-healing software system. Everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well. And you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened, what's going to happen. And so you get to these applications like predictive maintenance, or smarter systems, and increasingly you want to do that stuff not just intelligently, but fast in real time. So millisecond response, so that when you're driving a self-driving car, and the system realizes that you're about to do something, essentially you want to be able to act in something that looks like real time. All systems want to do that, they want to be more intelligent, and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a market. >> It's interesting near real time isn't good enough when you need real time. >> Yeah, it's not, it's not. And it's like everybody wants real even when you don't need it, ironically you want it. It's like having the feature for, you know you buy a new television, you want that one feature, even though you're not going to use it. You decide that's your buying criteria. Real time is criteria for people. >> So I mean, what you're saying then is near realtime is getting closer to real time as fast as possible? >> Right. >> Okay, so talk about the aspect of data, 'cause we're hearing a lot of conversations on theCUBE in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get know how to fix it. So this is a big part of what we're seeing with people saying, "Hey, you know I want to "make my machine learning algorithms better "after the fact, I want to learn from the data." How do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data, knowing when it happened? >> Well, for sure what you're saying is, is none of this is non-linear, it's all incremental. And so if you take something, you know just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop which is, I instrument it, I watch what happens, oh that's wrong, oh I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car. But every system moves along that evolution. And so you get the dynamic of constantly instrumenting, watching the system behave and do it. And so a self driving car is one thing, but even in the human genome, if you look at some of our customers, you know, people like, people doing solar arrays, people doing power walls like all of these systems are getting smarter and smarter. >> Well, let's get into that. What are the top applications? What are you seeing with InfluxDB, the time series, what's the sweet spot for the application use case and some customers? Give some examples. >> Yeah so it's pretty easy to understand on one side of the equation, that's the physical side is, sensors are getting cheap obviously we know that. The whole physical world is getting instrumented, your home, your car, the factory floor, your wrist watch, your healthcare, you name it, it's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but they're all on that side, they're all about IOT. So they're thinking about consumer IOT kind of projects like Google's Nest, Tudor, particle sensors, even delivery engines like Rappi, who deliver the instant car to South America. Like anywhere there's a physical location and that's on the consumer side. And then another exciting space is the industrial side. Factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational. Because what has to get smarter when you're building a factory is systems all have to get smarter. And then lastly, a lot in the renewables, so sustainability. So a lot, you know, Tesla, Lucid motors, Nicola motors, you know, lots to do with electric cars, solar arrays, windmills arrays, just anything that's going to get instrumented that where that instrumentation becomes part of what the purpose is. >> It's interesting the convergence of physical and digital is happening with the data. IOT you mentioned, you know, you think of IOT, look at the use cases there. It was proprietary OT systems, now becoming more IP enabled, internet protocol. And now edge compute, getting smaller, faster, cheaper. AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? Where's the IOT OT dots connecting to? Because, you know as these two cultures merge, operations basically, industrial, factory, car, they got to get smarter. Intelligent edge is a buzzword but I mean, it has to be more intelligent. Where's the action in all this? >> So the action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the action's really happen at the developer. And so what you're seeing is a movement in the world that maybe you and I grew up in with IT or OT moving increasingly that developer driven capability. And so all of these IOT systems, they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business? What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express themselves? Or am I trying to figure out when the next heart rate monitor is going to show up in my apple watch? Right, what am I trying to do? What's the system I need to build? And so starting with the developer is where all of the good stuff happens here. Which is different than it used to be, right. It used to be you'd buy an application or a service or a SaaS thing for, but with this dynamic, with this integration of systems, it's all about bespoke, it's all about building something. >> So let's get to the developer real quick. Real highlight point here is the data, I mean, I could see a developer saying, "Okay, I need to have an application for the edge," IOT edge or car, I mean we're going to have, I mean Tesla got applications of the car, it's right there. I mean, there's the modern application life cycle now. So take us through how does this impacts the developer. Does it impact their CICD pipeline? Is it cloud native? I mean where does this go to? >> Well, so first of all you're talking about, there was an internal journey that we had to go through as a company which I think is fascinating for anybody that's interested, is we went from primarily a monolithic software that was open sourced to building a Cloud-native platform. Which means we had to move from an agile development environment to a CICD environment. So to degree that you are moving your service, whether it's you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right, to a degree that that service is cloud. Then increasingly we remove from an agile development to a CICD environment, which you're shipping code to production every day. And so it's not just the developers, it's all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also going to happen in a big way. >> When your customer base that you have now, and as you see evolving with in InfluxDB, is it that they're going to be writing more of the application or relying more on others? I mean obviously it's an open source component here. So when you bring in kind of old way, new way, old way was, I got a proprietary platform running all this IOT stuff, and I got to write, here's an application that's general purpose. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does this job. >> A good way to think about this is- >> Versus new way which is what? >> So yeah a good way to think about this is what's the role of the developer/architect, CTO, that chain within a large, with an enterprise or a company. And so the way to think about is I started my career in the aerospace industry. And so when you look at what Boeing does to assemble a plane, they build very very few of the parts. Instead what they do is they assemble. They buy the wings, they buy the engines, they assemble, actually they don't buy the wings. That's the one thing, they buy the material for the wing. They build the wings 'cause there's a lot of tech in the wings, and they end up being assemblers, smart assemblers of what ends up being a flying airplane. Which is a pretty big deals even now. And so what happens with software people is, they have the ability to pull from you know, the best of the open source world. So they would pull a time series capability from us, then they would assemble that with potentially some ETL logic from somebody else. Or they'd assemble it with a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers but they become masters of that bespoke application. And I think that's where it goes 'cause you're not writing native code for everything. >> So they're more flexible, they have faster time to market 'cause they're assembling. >> Way faster. >> And they get to still maintain their core competency, AKA their wings in this case. >> They become increasingly not just coders but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff. By the way, this is not different than the people just up the road. Google have been doing for years or the tier one Amazon building all their own. >> Well, I think one of the things that's interesting is that this idea of a systems developing, a system architecture. I mean systems have consequences when you make changes. So when you have now cloud data center on-premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >> That's exactly, but that's where that Boeing or that airplane building analogy comes in. For us, we've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on prem edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that, you want to make sure at least that base layer, that database layer that those components talk to each other. >> So I'll have to ask you if I'm the customer, I put my customer hat on. Okay, hey, I'm dealing with a lot. >> Does that mean you have a PO for- >> (laughs) A big check, a blank check, if you can answer this question. >> Only if in tech. >> If you get the question right. I got all this important operation stuff, I got my factory, I got my self-driving cars, this isn't like trivial stuff, this is my business. How should I be thinking about time series? Because now I have to make these architectural decisions as you mentioned and it's going to impact my application development. So huge decision point for your customers. What should I care about the most? What's in it for me? Why is time series important? >> Yeah, that's a great question. So chances are, if you've got a business that was 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that, you built something on Oracle, or you built something on IBM's Db2, right, and you made it work within your system. Right, and so that's what you started building. So it's already out there, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time, I hate the word, but digital transformation. Then you start with time series, it's a foundational base layer for any system that you're going to build. There's no system I can think of where time series shouldn't be the foundational base layer. If you just want to store your data and just leave it there and then maybe look it up every five years, that's fine. That's not time series. Time series is when you're building a smarter more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems the more obvious it becomes. >> And since I have a PO for you and a big check. >> Yeah. >> What's the value to me when I implement this? What's the end state? What's it look like when it's up and running? What's the value proposition for me? What's in it for me? >> So when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, the transforming it in near real time. So that the other dependencies that a system it gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's doing every action that's above, but it's foundational to build a really compelling intelligence system. I think that's what developers and architects are seeing now. >> Bottom line, final word, what's in it for the customer? What's your statement to the customer? What would you say to someone looking to do something in time series and edge? >> Yeah so it's pretty clear to us that if you're building, if you view yourself as being in the business of building systems, that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time, that you start from time series. But I also want to say what's in it for us, Influx. What's in it for us is, people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare, it's hard not to be proud or feel like, "Wow." >> Yeah. >> "Somehow I've been lucky, I've arrived at the right time, "in the right place with the right people "to be able to deliver on that." That's also exciting on our side of the equation. >> Yeah, it's critical infrastructure, critical of operations. >> Yeah. >> Great stuff. Evan thanks for coming on, appreciate this segment. All right, in a moment, Brian Gilmore director of IOT and emerging technology at InfluxData will join me. You're watching theCUBE, leader in tech coverage. Thanks for watching. (upbeat music)

(soft techno music) >> Good evening. Welcome back to the Kube. Live in Los Angeles. We are at KubeCon Cloud Native Con 2021. Lisa Martin with Dave Nicholson, rounding out our day. We're going to introduce you to a new company, a new company that's new to us. I should say, log DNA. Peter Choi joins us the VP of product. Peter, welcome to the program. >> Thanks for having me. >> (Lisa) Talk to us about log DNA. Who are you guys? What do you do? >> So, you know, log DNA is a log medicine platform. Traditionally, we've been focused on, you know, offering log analysis, log management capabilities to dev ops teams. So your classic kind of troubleshooting, debugging, getting into your systems. More recently, maybe in like the last year or so we've been focused on a lot of control functionality around log medicine. So what I mean by that is a lot of people typically think of kind of the analysis or the dashboards, but with the pandemic, we noticed that you see this kind of surge of data because all of the services are being used, but you also see a downward pressure on cost, right? Because all of a sudden you don't want to be spending two X on those digital experiences. So we've been focused really on kind of tamping down kind of controls on the volume of log data coming in and making sure that they have a higher kind of signal and noise ratio. And then, you know, I'll talk about it a little bit, but we've really been honing in on how can we take those capabilities and kind of form them more in a pipeline. So log management, dev ops, you know, focusing on log data, but moving forward really focused on that flow of data. >> (Dave) So, when you talk about the flow of data and logs that are being read, make this a little more real, bring it up, bring it up just to level in terms of data, from what? >> Yeah. >> What kind of logs? What things are generating logs? What's the relevant information that's being. Kept track of? >> Yeah, I mean, so from our perspective, we're actually agnostic to data source. So we have an assist log integration. We have kind of basic API's. We have, you know, agents for any sort of operating system. Funny enough people actually use those agents to install, log DNA on robots, right? And so we have a customer they're, you know, one of the largest E-commerce platforms on, in the, in the world and they have a warehouse robots division and they installed the agent on every single one of those robots. They're, you know, they're running like arm 64 processors and they will send the log data directly to us. Right? So to us, it's no different. A robot is no different from a server is no different from an application is no different from a router. We take in all that data. Traditionally though, to answer your question, I guess, in the simplest way, mostly applications, servers, firewalls, all the traditional stuff you'd expect kind of going into a log platform. >> So you mentioned a big name customer. I've got a guess as to who that is. I won't, I won't say, but talk to us about the observability pipeline. What is that? What are the benefits in it for customers? >> (Peter) Sure. So, like if we zoom out again, you know, you think about logs traditionally. I think a lot of folks say, okay, we'll ingest the logs. We'll analyze them. What we noticed is that there's a lot of value in the step before that. So I think in the earlier days it was really novel to say, Hey, we're going to get logs and we're going to put it into a system. We're going to analyze it. We're going to centralize. Right. And that had its merits. But I think over time it got a little chaotic. And so you saw a lot of the vendors over the last three years consolidating and doing more of a single pane of glass, all the pillars of observability and whatnot. But then the downside of that is you're seeing a lot of the teams that are using that then saying being constrained by single vendor for all the ways that you can access that data. So we decided that the control point being on the analysis side on, on the very far right side was constricting. So we said, okay, let's move the control point up into a pipeline where the logs are coming to a single point of ingress. And then what we'll do is we will offer views, but also allow you to stream into other systems. So we'll allow you to stream into like a SIM or a data warehouse or something, something like that. Right? So, and you know, we're still trying to like nail down the messaging. I'm sure our marketing person's going to roast me after this. But the simplest way to think of observability pipeline is it's the step before the analysis part, that kind of ingest processes and routes the data. >> (Dave) Yeah. This is the Kube, by the way, neither one of us is a weather reporter. (laughing) So, so the technical stuff is good with us. >> Yes. It is. What are, and talk to us about some of the key features and capabilities and maybe anything that's newly announced are going to be announced. >> Yeah. For sure. So what we recently announced early access on is our streaming capabilities. So it's something that we built in conjunction with IBM and with a couple of, you know, large major institutions that we were working with on the IBM cloud. And basically we realized as we were ingesting a log data, some of those consumers wanted to access subsets of that data and other systems such as Q radar or, you know, a security product. So we ended up taking, we filtered down a subset of that data and we stream it out into those systems. And so we're taking those capabilities and then bringing it into our direct product, you know, whatever you access via logging.com. That is what's essentially going to be the seed for the kind of observability pipeline moving forward. So when you start thinking about it, all of this stuff that I mentioned, where we say, we're focusing on control, like allowing you to exclude logs, allowing you to transform logs, you take those processing capabilities, you take the streaming capabilities, you put them together and all of a sudden that's the pipeline, right? So that's the biggest focus for us now. And then we also have supporting features such as, you know, control API's. We have index rate alerting so that you can get notified if you see aberrations in the amount of flow of data. We have things like variable retention. So when a certain subset of logs come in, if you want it store it for seven days or 30 days, you can go ahead and do that because we know that a large block of logs is going to have many different use cases and many different associated values, right? >> So let's pretend for a moment that a user, somebody who has spent their money on log DNA is putting together a Yelp review and they've given you five stars. >> Yup. >> What do they say about log DNA? Why did they give you that five star rating? >> Yeah. Absolutely. I think, you know, the most common one and it's funny it's Yelp because we actually religiously mine, our G2 crowd reviews. (all laughing) And so the thing that we hear most often is, it's ease of use, right? A lot of these tools. I mean, I'm sure, you know, you're talking to founders and product leaders every day with developers. Like the, the bar, the baseline is so low, you know, a lot of, a lot of organizations where like, we'll give them the, you know, their coders, they'll figure it out. We'll just give them docs and they'll figure it out. But we, we went a little bit extra in terms of like, how can we smooth that experience so that when you go to your computer and you type in QTPL, blah, blah, blah, two lines, and all of a sudden all your logs are shipping from your cluster to log DNA. So that's the constant theme for us in all of our views is, Hey, I showed up, I signed up and within 30 minutes I had everything going that I needed to get. >> (Lisa) So fast time to value. >> Yes. >> Which is critical these days. >> Absolutely. >> Talk to me. So here we are at, at KubeCon, the CNCF community is huge. I think I, the number I saw yesterday was 138,000 contributors. Lots of activity, because we're in person, which is great. We can have those hallway networking conversations that we haven't been able to have in a year and a half. What are some of the things that you guys have heard at the booth in terms of being able to engage with the community again? >> You know, the thing that we've heard most often is just like having a finger on the pulse. It's so hard to do that because you know, when we're all at our computers, we just go from zoom to zoom. And so it, it like, unless it punches you in the face, you're not aware of it. Right. But when you come here, you look around, you go, you can start to identify trends, you hear the conversations in the hallway, you see the sessions. It's just that, that sense of, it's almost like a Phantom limb that, that sense of community and being kind of connected. I think that's the thing that we've heard most often that people are excited. And, you know, I think a lot of us are just kind of treating this like a dry run. Like we're kind of easing our way back in. And so it, you know, it felt good to be back. >> Well, they've done a great job here, right? I mean, you have to show your proof of vaccination. They're doing temperature checks, or you can show your clear health pass. So they're making it. We were talking to the executive director of CNCF earlier today and you're making it, it's not rocket science. We have enough data to know that this can be done carefully and safely. >> (David) Don't forget the wristbands. >> That's right. And, and did you see the wristbands? >> (Peter) Oh yeah. >> Yeah, yeah that's great. >> Yep, it is great. >> I was, I was on the fence by the way. I was like, I was a green or yellow, depending on the person. >> (both) Yeah. >> Yeah. But giving, giving everybody the opportunity to socialize again and to have those, those conversations that you just can't have by zoom, because you have somebody you've seen someone and it jogs your memory and also the control of do I want to shake someone's hand or do I not. They've done a great job. And I think hopefully this is a good test in the water for others, other organizations to learn. This can be done safely because of the community. You can't replicate that on video. >> (Peter) Absolutely. And I'll tell you this one for us, this is our, this is our event. This is the event for us every single year. We, we it's the only event we care about at the end of the day. So. >> What are some of the things that you've seen in the last year, in terms of where, we were talking a lot about the, the adoption of Kubernetes, kind of, where is it in its maturation state, but we've seen so much acceleration and digital transformation in the last 18 months for every industry businesses rapidly pivoting multiple times to try to, to survive one and then figure out a new way to thrive in this, this new I'll call it the new. Now I'll borrow that from a friend at Citrix, the new now, not the new normal, the new now, what are some of the things that you've seen in the last year and a half from, from your customer base in terms of what have they been coming to you saying help? >> (Peter) You know, I think going back to the earlier point about time to value, that's the thing that a lot. So a lot of our customers are, you know, very big Kubernetes, you know, they're, they're big consumers of Kubernetes. I would say, you know, for me, when I do the, we do our, our QBRs with our top customers, I would say 80% of them are huge Kubernetes shops. Right. And the biggest bottleneck for them actually is onboarding new engineers because a lot of the, and you know, we have a customer, we have better mortgage. We have, IBM, we have Rappi is a customer of ours. They're like Latin American version of Instacart. They double their engineering base and you, you know, like 18 in 18 months. And so that's, you know, I think it was maybe from 1500 to 3000 developers or so, so their thing is like, we need to get people on board as soon as possible. We need to get them in these tools, getting access to, to, to their longs, to whatever they need. And so that's been the biggest thing that we've heard over and over again is A, how can we hire? And then B when we hire them, how do we onboard them as quickly as possible, so that they're ramped up and they're adding value. >> How do you help with that onboarding, making it faster, seamless so that they can get value faster? >> So for us, you know, we really lean in on our, our customer success teams. So they do, you know, they do trainings, they do best practices. Basically. We kind of think of ourselves given how much Kubernetes contradiction we have, we think of ourselves as cross pollinators. So a lot of the times we'll go into those decks and we'll try to learn just as much as we're trying to try to teach. And then we'll go and repeat that process through every single set of our customers. So a lot of the patterns that we'll see are, well, you know, what kinds of tools are you using for orchestration? What kind of tools are you using for deployment? How are you thinking about X, Y, and Z? And then, you know, even our own SRE teams will kind of get into the mix and, you know, provide tips and feedback. >> (Lisa) Customer centricity is key. We've heard that a lot today. We hear that from a lot of companies. It's one thing to hear it. It's another thing to see it. And it sounds like the Yelp review that you would have given, or, or what you're hearing through G2 crowd. I mean, that voice of the customer is valid. That's, that's the only validation. I think that really matters because analysts are paid. >> Yeah. >> But hearing that validation through the voice of the customer consistently lets you know, we're going in the right direction here. >> Absolutely. >> I think it's, it's interesting that ease of use comes up. You wonder if those are only anonymous reviews, you don't necessarily associate open source community with cutting edge, you know, we're the people on the pirate ship. >> (Peter) Yeah. And so when, when, when people start to finally admit, you know, some ease of use would be nice. I think that's an indication of maturity at a certain point. It's saying, okay, not everyone is going to come in and sit behind a keyboard and program things in machine language. Every time we want to do some simple tasks, let's automate, let's get some ease of use into this. >> And I'll tell you in the early days it drove me and our, our CEO talker. It drove us nuts that people would say easiest to be like, that's so shallow. It doesn't mean anything. Well, you know, all of that. However, but to your point, if we don't meet the use case, if we don't have the power behind it, the ease of use is abstracting away. It's like an iceberg, right. It's abstracting away a lot. So we can't even have the ease of use conversation unless we're able to meet the use case. So, so what we've been doing is digging into that more, be like, okay, ease of use, but what were you trying to do? What, what is it that we enabled? Because ease of use, if it's a very shallow set of use cases is not as valid as ease of use for petabytes of data for an organization like IBM. Right? >> That's a great, I'm glad that you dug into that because ease of use is one of those things that you'll see it in marketing materials, but to your point, you want to know what does this actually mean? What are we delivering? >> Right. >> And now, you know what you're delivering with Peter, thank you for sharing with us about logged in and what you guys are doing, how you're helping your community of customers and hearing the voice of the customer through G2 and others. Good work. >> Thank you. And by the way, I'll be remiss if I, if I don't say this, if you're interested in learning more about some of the stuff that we're working on, just go to logging in dot com. We've got, I think we've got a banner for the early access programs that I mentioned earlier. So, you know, at the end of the day, to your point about customer centricity, everything we prioritize is based on our customers, what they need, what they tell us about. And so, you know, whatever engagement that we get from the people at the show and prospects, like that's how we drive a roadmap. >> (Lisa) Yup. That's why we're all here. Log dna.com. Peter, thank you for joining Dave and me today. We appreciate it. >> Thanks for having me. >> Our pleasure for Dave Nicholson. I'm Lisa Martin signing off from Los Angeles today. The Kubes coverage of KubeCon clouding of con 21 continues tomorrow. We'll see then. (soft techno music)