 Time series data is any data that's stamped in time in some way. That could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world, like devices or sensors, temperature gauges, batteries, any device really, or things in the virtual world. Could be software, maybe it's software in the cloud or data in containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time and there are many examples in our everyday lives. All you got to do is punch up any stock ticker and look at its price over time in graphical form and that's a simple use case that anyone can relate to. And you can build timestamps into a traditional relational database. You just add a column to capture time. And as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now the problem with the latter example that I just gave you is that you got to hunt and peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases, they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about IoT and edge scale where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. They're purpose built and can much more efficiently support ingesting metrics at scale and then comparing data points over time. Time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective. Instead of throwing processing power at the problem, for example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale, time series databases are simply a better fit for the job. Welcome to moving the world with Influx DB made possible by Influx Data. My name is Dave Vellante and I'll be your host today. Influx Data is the company behind Influx DB, the open source time series database. Influx DB is designed specifically to handle time series data, as I just explained. We have an exciting program for you today and we're going to showcase some really interesting use cases. First, we'll kick it off in our Palo Alto studios where my colleague, John Furrier will interview Evan Kaplan who's the CEO of Influx Data. After John and Evan set the table, John's going to sit down with Brian Gilmore. He's the director of IoT and emerging tech at Influx Data. And they're going to dig into where Influx Data is gaining traction and why adoption is occurring and why it's so robust. And they're going to have tons of examples and double click into the technology. And then we bring it back here to our East Coast studios where I get to talk to two practitioners doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away, Palo Alto. Okay, today we welcome Evan Kaplan, CEO of Influx Data, the company behind Influx DB. Welcome Evan, thanks for coming on. Hey John, thanks for having me. Great segment here on the Influx DB story. What is the story? Take us through the history, why time series, what's the story? So the history, the 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 just developed this real clear point of view, is 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 in Influx DB at the end of 2013. And basically, 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 in the company, what's the history of his employees and stuff? Yeah, I think we're, I always forget the number, but it's 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. Because 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 to 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 clouds started coming in, you're starting to see more databases, proliferate types of databases. And time series in particular is interesting because 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 is not worth it after the time. Sometimes it's worth it. And then you get the data lakes. 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? Yeah, the time series. Why is time series an important momentum? What's the bottom line? 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 increasingly 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 gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you wanna 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 wanna be able to act in something that looks like real-time. All systems wanna do that. They wanna be more intelligent and they wanna be more real-time. And so we just happen to, 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. And it's like everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you buy a new television, you want that one feature. Even though you're not going to use it, you decide that you're buying criteria. Real-time is a buying criteria for people. So what you're saying then is near real-time is getting closer to real-time as fast as possible. Right. Okay, so talk about the aspect of data because 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, I want to make my machine learning algorithms better after the fact, I want to learn from the data. How does that, 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, so for sure what you're saying is none of this is non-linear, it's all incremental. And so if you take something, 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 four 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 self-driving cars, one thing, but even in the human genome, if you look at some of our customers, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. Well, let's get into that. What are the top applications? What are you seeing with Influx DB, the time series? What's the sweet spot for the application use case and some customers? Give some examples. So it's pretty easy to understand on one side of the equation, and that's the physical side is. Sensors are getting cheap, obviously, we know that. And the whole physical world is getting instrumented. Your home, your car, the factory floor, your wristwatch, 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, projects like Google's Nest, Tato, particle sensors, even delivery engines like Wrappy, who deliver the Instacart of 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 and sustainability. So a lot, you know, Tesla, Lucid motors, Nikola 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? What was the, what's the IoT? Where's the IoT 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, where's the action in all this? So the action really, 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 interactions. So the actions 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 expressed themselves? Or am I trying to figure out when the next heart rate monitor is going to show up on my Apple Watch? What am I trying to do? What's the system I need to build? And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right? 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 gonna have, I mean, Tesla got applications of the cars 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 CI CD pipeline? Is it cloud native? I mean, where does this all, 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 who's interested. We went from primarily a monolithic software that was open source to building a cloud native platform, which means we had to move from an agile development environment to a CI CD environment. So to degree that you're moving your service, whether it's Tesla monitoring your car and updating your power walls, right? Or whether it's a solar company updating the arrays, right? To degree that that service is cloud, then increasingly we move from an agile development to a CI CD environment, which is shipping code to production every day. And so it's not just the developers, 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 known and you see evolving with Influx DB, is it that they're going to be writing more of the application or relying more on others? I mean, obviously there's open source component here. So when you bring in kind of old way, new way, old way was I got a proprietary application platform running all this IOT stuff and I got a 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 its job. A good way to think about this is what? So yeah, good way to think about this is what's the role of the developer slash architect, CTO, that chain within a large enterprise or company. And so the way to think about it 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. It's the one thing, they buy the material for the wings. They build the wings because 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 deal even now. And so what happens with software people is they have the ability to pull from 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 because you're not writing native code for everything. So they're more flexible. They have faster time to market because they're assembling. Way faster. They get to still maintain their core competency aka the 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. It 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 wanna make sure at least that base layer, that database layer that those components talk to each other. We'll have to ask you if I'm the customer I'll put my customer hat on. Okay, hey I'm dealing with a lot. Does that mean you have a P.O. for me? A big check, a blank check. If you can answer this question. Only if the tech. If you get the question right. I've got all this important operations stuff. I've got my factory, I've 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 gonna 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 and you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series shouldn't be the foundational base layer. If you just wanna 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, 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 an end state? 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 the other dependencies that a system that 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 intelligent 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 of the customer? What would you say to someone? Looking to do something in time series on 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 them to be increasingly intelligent, self-healing autonomous, you want them 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, somehow I've been lucky. I've arrived at the right time and 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 operations. Yeah, yeah. Great stuff. Evan, thanks for coming on, appreciate the segment. All right, in a moment, Brian Gilmore, director of IoT and emerging technology that influx state will join me. You're watching theCUBE, leader in tech coverage. Thanks for watching. Time series data from sensors, systems, and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamped data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects, stores, and analyzes data. Influx DB empowers developers to extract valuable insights and turn them into action by building transformative IoT, analytics, and cloud native applications. Purpose built and optimized to handle the scale and velocity of timestamped data. Influx DB puts the power in your hands with developer tools that make it easy to get started quickly with less code. Influx DB is more than a database. It's a robust developer platform with integrated tooling that's written in the languages you love so you can innovate faster. Run influx DB anywhere you want by choosing the provider and region that best fits your needs. Across AWS, Microsoft Azure, and Google Cloud, Influx DB is fast and automatically scalable so you can spend time delivering value to customers not managing clusters. Take control of your time series data so you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with Influx DB. Visit influxdata.com slash cloud to learn more. Okay, now we're joined by Brian Gilmore, director of IoT and emerging technologies at Influx Data. Welcome to the show. Thank you, John, great to be here. We just spent some time with Evan going through the company and the value proposition within Influx DB. What's the momentum? Where do you see this coming from? What's the value coming out of this? Well, I think we're sort of hitting a point where the technology is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations. Everybody from like the most well-funded sort of advanced big technology companies to the smaller academics, the startups. And the managing of that sort of data that emits from that technology is time series and us being able to give them a platform, a tool that's super easy to use, easy to start. And then of course we'll grow with them is has been key to us sort of, you know, riding along with them as they're successful. Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now you go back since 2013, 14, even like five years ago, that convergence of physical and digital coming together, IP enabled edge. Edge has always been kind of hyped up, but why now, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? I think it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is that there's like a hybrid nature to all of these applications where there is definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified, uniform way. That's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the outer reaches of outer space or whether it's optimizing the factory floor. Yeah, I think one of the things you also mentioned, the genome too, big data's coming to the real world. And I think IoT has been kind of like this thing for OT and some use case. But now with the cloud, all companies have an edge strategy now. So what's the secret sauce? Because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce? Well, I mean, I think part of it is just that the technology is becoming more capable. And that's especially on the hardware side, right? I mean, like technology computers getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the microcontroller layer with our, you know, our client libraries and then working hard to make our applications, especially the database, as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is fantastic. Now you can take that, you can run that locally, you can do your local decision making, you can use Inflex DB as sort of an input to automation control, the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized AI and machine learning and all of that. What's interesting is the open source success has been something that we've talked about a lot and theCUBE about how people are leveraging that. You guys have users in the enterprise, users at IoT market, but you got developers now. Yeah. Kind of together, brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with Inflex DB? Yeah, I mean, I think there are the developers who are building companies, right? And then these are the startups and the folks that we love to work with who are building new services, new products, things like that. And especially on the consumer side of IoT, there's a lot of that, just those developers. But I think we got to pay attention to those enterprise developers as well, right? There are tons of people with the title of engineer in your regular enterprise organizations and they're there for systems integration. They're there for looking at what they would build versus what they would buy. And a lot of them come from a strong open source background and they know the communities, they know the top platforms in those spaces and they're excited to be able to adopt and use to optimize inside the business as compared to just building a brand new one. You know, it's interesting too when Evan and I were talking about open source versus closed OT systems. So how do you support the backwards compatibility of older systems while maintaining dozens of data formats out there, bunch of standards, protocols, new things are emerging, everyone wants to have a control plane, everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable, it's open source, it's an edge agent, you can run it as close to the edges you'd like. It speaks dozens of different protocols in its own right, a couple of which, MQTT, OPCUA are very, very applicable to these IoT use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware into InfluxDB. These are companies like Capware and Highbyte who are really experts in those downstream industrial protocols. I mean, that's a business not everybody wants to be in, it requires some very specialized, very hard work and a lot of support. And so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. What's some of the customer testimonies that they share with you? Can you share some anecdotal, kind of like, well, that's the best thing I've ever used, it's really changed my business. Or this is a great tech that's helped me in these other areas. What are some of the sound bites you hear from customers when they're successful? Yeah, I mean, I think it ranges. You've got customers who are just finally being able to do the monitoring of assets, sort of at the edge in the field. We have a customer who has these tunnel boring machines that go deep into the earth to drill tunnels for cars and trains and things like that. They are just excited to be able to stick a database onto those tunnel boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been safely stored and then it can just very quickly and instantly connect up to their centralized database. Just having that visibility is brand new to them and that's super important. On the other hand, you have the customers who are way far beyond the monitoring use case where they're actually using the historical records and the time series database to, I think Evan mentioned, forecast things. For predictive maintenance, being able to pull in the telemetry from the machines but then also all of that external enrichment data, the metadata, the temperatures, the pressures, who is operating the machine, those types of things and being able to easily integrate with platforms like Jupyter Notebooks or all of those scientific computing and machine learning libraries to be able to build the models, train the models and then they can send that information back down to Influx TV to apply it and detect those anomalies which are... I think that's going to be an area, I personally think that's a hot area because I think, if you look at AI right now, it's all about training the machine learning algorithms after the fact. So time series becomes hugely important. Because now you're thinking, okay, the data matters, post-time. And then it gets updated, the new time. So it's like constant data cleansing, data iteration, data programming. We're starting to see this new use case emerge in the data field. Yeah, I mean, I think... You agree? Yeah, of course, yeah. The ability to sort of handle those pipelines of data smartly, intelligently, and then to be able to do all of the things you need to do with that data in stream before it hits your sort of central repository. And we make that really easy for customers. Like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the partner data, but also it has a whole bunch of capabilities around being able to process that data, enrich it, reformat it, route it, do whatever you need. So at that point, you're basically able to... You're playing your data in exactly the way you would want to do it. You're routing it to different destinations. And it's not something that really has been in the realm of possibility until this point. Yeah, and when Evan was on, it's great. He was a CEO, so he sees the big picture with customers. He kind of put the package together and said, hey, we got a system, we got customers. People wanted to leverage our product. What's your PEO? He's selling too, as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the inflex DB system architect. He got developers and users. Can you talk about that reality as customers start to commercialize and operationalize this from a commercial standpoint? You got a relationship to the cloud. The edge is there. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? Yeah, I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's generally like compartmentalized to a point of like, you know, a single asset or a single factory, a line, whatever. But what people do, they want to be able to make the decisions there at the edge locally, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually down sample that data or they can, you know, detect like the really important metrics or the anomalies, and then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset. And then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detection. So in this model, you have a lot of commercial operations, industrial equipment, the physical plant, physical business with virtual data cloud, all coming together. What's the future for Influx DB from a tech standpoint? Because you got open. Yep. There's an ecosystem there. Yep. There's customers who want operational reliability. For sure. I mean, so you got organic. Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars, right? So I don't know if we can like absolutely perfectly predict what's coming, but I think there are some givens. And I think those givens are going to be that the world is only going to become more hybrid, right? And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data being generated at the edge. And then there's going to be data generated sort of at all points in between, like physical locations as well as things that are very virtual. And I think, you know, we're building some technology right now that's going to allow the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of solutions that we'll be bringing to the customer sort of over the next little bit. But I also think we have to start thinking about like what happens when the edge is actually off the planet, right? I mean, we've got customers, you're going to talk to two of them in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or, you know, all the way sort of on the other side of the universe. And to be able to process data like that and to do so in a way, it's, we got to build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. So that we'll be ready for that one. I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of a new thinking is hyperscale has always been built up, full stack developers, even the old OT world, Evan was pointing out that they built everything, right? And the world's going to more assembly with core competency and IP and also property, being the core of their Apple so faster assembly and building, but also integration. You got all this new stuff happening. And that's to separate out the data complexity from the app. So space, genome, driving cars, throws off massive data. It does. So is Tesla, is the car the same as the data layer? I mean, yeah, it's certainly a point of origin. I think the thing that we want to do is we want to let the developers work on the world changing problems, the things that they're trying to solve, whether it's energy or any of the other health or other challenges that these teams are building against. And we'll worry about that time series data in the underlying data platforms so that they don't have to, right? I mean, I think you talked about it, for them just to be able to adopt the platform, quickly integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's going to allow them to be more iterative. They're going to be able to do more testing and things like that. And ultimately, it'll accelerate the adoption and the creation of technology. You mentioned earlier in our talk about unification of data. How about APIs? Because developers love APIs in the cloud. Unifying APIs, how do you view that? Yeah, I mean, we are APIs. That's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is built on our public APIs. And it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now. And we bring a very sort of useful set of them for managing the time series data these guys are all challenged with. It's interesting. You and I were talking before we came on camera about how data field is going to have this kind of SRE role that DevOps had, Site Reliability Engines, which managed a bunch of servers. There's so much data out there now. Yeah. Yeah, it's like reining data for sure. And I think that ability to, like one of the best jobs on the planet is going to be to be able to sort of be that data wrangler, to be able to understand what the data sources are, what the data formats are, how to be able to efficiently move that data from point A to point B, to process it correctly. So that the end users of that data aren't doing any of that sort of hard upfront preparation, collections or its work. That's data as code. I mean, data engineering is becoming a new discipline. It for sure. And the democratization is the benefit. Yeah. Everyone, data science get easier. I mean, data science, they want to make it easy, right? Yeah. They want to do the analysis, right? Yeah, I mean, I think it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are, right? So like we have the sort of client libraries that allow them to just port to us, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point, nobody's going to know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, their end users inside their organization. Well, Brian, great segment, great insight. Thanks for sharing all the complexities and IOT that you guys helped take away with APIs and assembly and all the system architectures that are changing. Edge is real, cloud is real. Absolutely. Mainstream enterprises and you got developer attraction too. So congratulations. Yeah, it's great. Any last word you want to share? Give a deal. No, just, I mean, please, if you're going to check out Influx TV, download it, try out the open source, contribute if you can. That's a huge thing. It's part of being the open source community. You know what, definitely just use it. I think once people use it, they try it out, they'll understand very, very quickly. Open source with developers, enterprise and Edge coming together. All together. All together, you're going to hear more about that in the next segment too. All right, thanks for coming on. Okay, when we return, Dave Vellante will lead a panel on Edge and data in Influx DB. You're watching theCUBE, the leader in high tech enterprise coverage. As a startup, we move really fast, we find that Influx DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing, and we see a bright future in working with Influx. My name is Aaron Semly, I'm the CTO at Highlight. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops. Treat that as an asset to deliver the IT systems to enable applications like overall equipment effectiveness that can help the factory use better, smarter, faster. Time series data and manufacturing is really important. If you take a piece of equipment, you have a temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding, you can't do what manufacturers ultimately want to do, which is predict the future. Influx DB represents kind of a new way to store time series data with some more advanced technology, and more importantly, more open technologies. The other thing that Influx does really well is once the data is Influx, it's very easy to get out, right? They have a modern REST API and other ways to access the data that would be much more difficult to do integrations with classic historians. iBike can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be OPC or a server's manufacturing execution systems, ERP, et cetera, and then push that seamlessly into Influx to then be able to run calculations. Manufacturing is changing this industrial 4.0. And what we're seeing is Influx being part of that equation, being used to store data off, unify namespace. We recommend Influx DB all the time to customers that are exploring a new way to share data manufacturing called unified namespace, who have open questions around, how do I share this new data that's coming through my UNS or MQTT broker? How do I store this and be able to query it over time? And we often point to Influx as a solution for that. It's a great brand, it's a great group of people, and it's a great technology. Okay, we're now going to go into the customer panel and we'd like to welcome Angelo Fausti, who's a software engineer at the Vera C Rubin Observatory and Caleb McLaughlin, who's Senior Spacecraft Operations Software Engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss, folks, this interview. Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, of course, doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem. Yeah, absolutely. And thanks for having me here, by the way. So Loft Orbital is a company that's a series B startup now who, and our mission basically, is to provide rapid access to space for all kinds of customers. Historically, if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, have big software teams, and then eventually worry about a bunch, like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as deploying a VM in AWS or GCP is getting your programs, your mission deployed on orbit with access to different sensors, cameras, radios, stuff like that. So that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve, there's a really cool company called Totem Labs who is working on building an IoT constellation for Internet of Things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT, which means you have this little modem inside a container that you track from anywhere in the world as it's going across the ocean. So there it's really little and they've been able to stay a small startup that's focused on their products, which is that super crazy complicated cool radio while we handle the whole space segment for them, which just before loft was really impossible. So that's our mission is providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving a huge variety of customers with all kinds of different missions and obviously generating a ton of data in space that we've got to handle. Yeah, so amazing, Caleb, what you guys do. I know you were lured to the skies very early in your career, but how did you kind of land in this business? Yeah, so I guess just a little bit about me. For some people, they don't necessarily know what they want to do like earlier in their life. For me, I was five years old and I knew I want to be in the space industry. So I started in the Air Force but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of actually. So I've kind of started out in satellites, I did spend some time in working in the launch industry on rockets. Then now I'm here back in satellites and honestly, this is the most exciting of the different space startups that I've been a part of. Super interesting. Okay, Angelo, let's talk about the Rubin Observatory. Varicy Rubin, famous woman scientist, Galaxy Guru, you guys, the observatory, you're way up high, you're gonna get a good look at the southern sky. I know COVID slowed you guys down a bit but no doubt you continue to code away on the software. I know you're getting close, you got to be super excited. Give us the update on the observatory and your role. All right, so yeah, Rubin is a state of the art observatory that is in construction on a remote mountain in Chile. And with Rubin, we'll conduct the large survey of space and time. We are going to observe the sky with an eight meter optical telescope and take a thousand pictures every night with a 3.2 gigapixel camera. And you are going to do that for 10 years, which is the duration of the survey. Yeah, amazing project. Now you earned a doctor of philosophy, so you probably spent some time thinking about what's out there. And then you went out to earn a PhD in astronomy and astrophysics. So this is something that you've been working on for the better part of your careers Yeah, that's right, about 15 years. I studied physics in college. Then I got a PhD in astronomy and I worked for about five years in another project, the Dark Energy Survey before joining Rubin in 2015. Yeah, impressive. So it seems like both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common, of course, is software and you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb, you could start. Yeah, absolutely. So the first company that I extensively used, InfluxDB, and was a launch startup called Astra, and we were in the process of designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. And when I joined the company, our data story was not very mature. We were collecting a bunch of data and lab view and engineers were taking that over to MATLAB to process it. And at first there, you know, that's the way that a lot of engineers and scientists are used to working. And at first that was like, people weren't entirely sure that that needed to change. But it's something, the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into Influx. And what was amazing to see and it's kind of the super cool moment with Influx is when we hooked that up to Grafana, Grafana is the visualization platform we use with Influx because it works really well with it. There was like this aha moment of our engineers who are used to this post-process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in Influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in Influx and it just was totally game changing for how we tested. So, Angelo, I was explaining in my open that, you know, you could add a column in a traditional RDBMS and do time series but with the volume of data that you're talking about and the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about Influx DB? Yeah, correct. So I work with the data management team and my first project was the record metrics that measure the performance of our software, the software that we used to process the data. So I started implementing that in a relational database but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found Influx DB. That was back in 2018. The another use for Influx DB that I'm also interested is the visits database. If you think about the observations, we are moving the telescope all the time in pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. In every point in that time series, we call that a visit. So we want to record the metadata about those visits in Influx DB. That time series is going to be 10 years long with about 1,000 points every night. It's actually not too much data compared to other problems. It's really just a different time scale. The telescope at the Rubin Observatory is pun intended, I guess, the star of the show. And I believe, I read that it's going to be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hubble's widest camera view, which is amazing. That's like 40 moons in an image, amazingly fast as well. What else can you tell us about the telescope? This telescope, it has to move really fast. And it also has to carry the primary mirror, which is an eight meter piece of glass. It's very heavy. And it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons. For that to work, the telescope needs to be very compact and stiff. And one thing that's amazing about its design is that the telescope, this 300 ton structure, it sits on a tiny film of oil, which has the diameter of human hair. And that makes an almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has in diameter the size of about seven full moons. And with that, we can map the entire sky in only three days. And of course, during operations, everything is controlled by software and it's automatic. There's a very complex piece of software called the scheduler, which is responsible for moving the telescope and the camera, which is recording 15 terabytes of data every night. And Angel, all this data lands in InfluxDB, correct? And what are you doing with all that data? Yeah, actually not. So we're using InfluxDB to record engineering data and metadata about the observations, like telemetry events and the commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because you have some high frequency data that the system needs to keep up. And we need to store this data and have it around for the lifetime of the project. Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about if you got these dishwasher-sized satellites you're kind of using a multi-tenant model. I think it's genius, but tell us about the satellites themselves. Yeah, absolutely. So we have in space some satellites already that as you said are dishwasher mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoebox to I guess a few times larger than what we have today. And it is, we do shoot to have effectively something like a multi-tenant model where we will buy a bus off the shelf. The bus is what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels. It has some radios attached to it. It handles the attitude control, basically steers the spacecraft in orbit. And then we build also in-house what we call our payload hub, which has all any customer payloads attached and our own kind of edge processing sort of capabilities built into it. And so we integrate that, we launch it. And those things, because they're in low earth orbit, they're orbiting the earth every 90 minutes. That's seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have one of the unique challenges of operating spacecraft in low earth orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, where we get to talk to them through our ground sites either in Antarctica or in the North Pole region. Talk more about how you use influx DB to make sense of this data from all this tech that you're launching into space. We basically, previously, we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was so slow and the size of our data would balloon over the course of a couple of days to the point where we weren't able to even store all of the data that we were getting. So we migrated to influx DB to store our time series telemetry from the spacecraft. So that's things like power level, voltage, currents, counts, whatever metadata we need to monitor about the spacecraft, we now store that in influx DB. And that has, now we can actually easily store the entire volume of data for the mission life so far without having to worry about the size bloating to an unmanageable amount. And we can also seamlessly query large chunks of data. Like if I need to see, for example, as an operator I might wanna see how my battery state of charge is evolving over the course of the year, I can have a plot in an influx that loads that in a fraction of a second for a year's worth of data because it does intelligent, I can intelligently group the data by a sliding time interval. So it's been extremely powerful for us to access the data. And as time has gone on, we've gradually migrated more and more of our operating data into influx. You know, let's talk a little bit about, we throw this term around a lot of data driven, a lot of companies say, oh yes, we're data driven but you guys really are. I mean, you got data at the core. Caleb, what does that mean to you? Yeah, so I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from a lot of kind of slow researching and digging into the data to like an instant instantaneous almost seeing the data, making decisions based on it immediately rather than having to wait for some processing. And that's something that I've also seen echoed in my current role, but to give another practical example, as I said, we have a huge amount of data that comes down every orbit and we need to be able to ingest all of that data almost instantaneously and provide it to the operator in near real time, you know, about a second worth of latency is all that's acceptable for us to react to see what is coming down from the spacecraft. And building that pipeline is challenging from a software engineering standpoint. Our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know in the goal of being data driven is publish metrics on individual, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. So we have kind of a production monitoring flow and what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. It's sometimes we've found ourselves before we started doing this, kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. But now that we're being a bit more data driven there, we are being much more effective in where we're spending our resources in our time, which is especially critical to us as we scale from supporting a couple satellites to supporting many, many satellites at once. Coach, you reduced those dead ends. Maybe Angela, you could talk about what sort of data driven means to you and your teams. I would say that having a real time visibility to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect with the telescope have good quality and that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible and then start fixing problems. Caleb, what are your sort of event intervals like? So I would say that as of today on the spacecraft, the level of timing that we deal with probably tops out at about 20 Hertz, 20 measurements per second on things like our gyroscopes. But I think the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example from when I worked on the rockets at Astra, there are baseline data rate that we would ingest data during a test is 500 Hertz. So 500 samples per second. And in some cases we would actually need to ingest much higher rate data, even up to like 1.5 kilohertz. So extremely, extremely high precision data there where timing really matters a lot. And I can, one of the really powerful things about inflex is the fact that it can't handle this. That's one of the reasons we chose it because there's times when we're looking at the results of a firing where you're zooming in, I talked earlier about how in my current job we often zoom out to look at a year's worth of data, you're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see the same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, hey, I opened this valve at exactly this time. And that goes, we wanna have that at, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at this exact moment. Was that before or after this valve opened? Those kind of, that kind of visibility is critical in these kind of scientific applications and absolutely game-changing to be able to see that in near real time and with a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. Can the scientists do self-serve or do you have to design and build all the analytics and queries for your scientists? I think that's absolutely, from my perspective, that's absolutely one of the best things about Influx and what I've seen be game-changing is that generally I'd say anyone can learn to use Influx and honestly, most of our users might not even know they're using Influx because what the interface that we expose to them is Grafana, which is a generic graphing, open-source graphing library that is very similar to Influx's own chronograph. And what it does is let it provides this almost, it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements and then you choose a particular, the particular fields you wanna look at. And again, that's a dropdown. So it's really easy for our users to discover and there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality that Influx provides. Putting data in the hands of those who have the context of domain experts is key. Angela, is it the same situation for you? Is it self-serve? Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards because they know exactly what they need to visualize. Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company, we weren't using Influx DB and we were dealing with serious issues of the database growing to an incredible size extremely quickly and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations. Guys, this has been really informative. It's pretty exciting to see how the edge is. Mountain tops, low earth orbits, space is the ultimate edge, isn't it? I wonder if you could answer two questions to wrap here. What comes next for you guys and is there something that you're really excited about that you're working on? Caleb, maybe you could go first and then Angela, you can bring us home. Basically, what's next for Loft Orbital is more satellites, a greater push towards infrastructure and really making, our mission is to make space simple for our customers and for everyone and we're scaling the company like crazy now, making that happen. It's extremely exciting time to be in this company and to be in this industry as a whole because there are so many interesting applications out there, so many cool ways of leveraging space that people are taking advantage of and with companies like SpaceX and the now rapidly lowering cost of launch, it's just a really exciting place to be in. We're launching more satellites, we are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of improvements that we're working on to really scale up our control software to be best in class and make it capable of handling such a large workload. You guys hiring? We are absolutely hiring, so we have positions all over the company, so we need software engineers, we need people who do more aerospace specific stuff, so absolutely I'd encourage anyone to check out the Loft Orbital website if this is at all interesting. All right, Hedjula, bring us home. Yeah, so what's next for us is really getting this telescope working and collecting data and when that's happened, it's going to be just a deluge of data coming out of this camera and handling all that data is going to be really challenging. Yeah, I wanna be here for that. I'm looking forward like for next year we have like an important milestone which is our commissioning camera which is a simplified version of the full camera. It's going to be on sky and so yeah, most of the system has to be working by them. Nice. All right guys, with that we're gonna end it. Thank you so much. Really fascinating and thanks to InfluxDB for making this possible. Really groundbreaking stuff, enabling value creation at the edge in the cloud and of course beyond at the space. Really transformational work that you guys are doing so congratulations and really appreciate the broader community. I can't wait to see what comes next from this entire ecosystem. Now in a moment, I'll be back to wrap up. This is Dave Vellante and you're watching theCUBE, the leader in high tech enterprise coverage. Welcome, Telegraph is a popular open source data collection agent. Telegraph collects data from hundreds of systems like IoT sensors, cloud deployments and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing or just show what you're doing with Telegraph. We'd love to hear what you're building. Thanks for watching Moving the World with InfluxDB made possible by InfluxData. I hope you learned some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes and you want to scale cost effectively with the highest performance and you're analyzing metrics and data over time, time series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link in the resources below. Remember all these recordings are going to be available on demand on theCUBE.net and InfluxData.com. So check those out and poke around InfluxData. They are the folks behind InfluxDB and one of the leaders in the space. We hope you enjoyed the program. This is Dave Vellante for theCUBE. We'll see you soon.