>>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 and 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 gotta do is punch up any stock, ticker and look at its price over time and 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 gotta 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 O T 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 Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna 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 gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna 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 >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, 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 is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, 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 outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, 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. Cuz 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 long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it 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 >>You mean what's causing us to grow. So >>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, broad sort of frame, which is where, 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 gonna 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 wanna be able to act in something that looks like real time, all systems want to do that, 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. >><laugh> yeah, it's not, it's not. And it's like, 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 know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube 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 how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, 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, so for sure, what you're saying is, 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 instrumented, 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, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, 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 for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting 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, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do 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, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna 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 yeah. Operations, basically industrial factory car, they gotta 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, 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 actions really happen at the developer. And so what you're seeing is a movement in the world that, 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 theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? 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 SA 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's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D 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, which I think is fascinating for anybody who'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 C I C D environment. So to a 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 degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're 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 gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. 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 versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> 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, the material for the w they build the wings, cuz 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 pretty big deal even now. And so what, 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, with potentially some ETL logic from somebody else, or they'd assemble it with, um, 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, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. 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 is that this idea of a systems developing a system architecture, I mean systems, uh, uh, 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 the that's where the, you know, that 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 pre 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. >>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. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, 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 gonna impact my application development. So huge decision point for your customers. What should I care about the most? So 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, you know, 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 a Oracle or you built something on IBM's DB two, 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, you know, 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 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, yeah. What is, what's the value to me as I, 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 >>So, 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, they're transforming it in near real time. So that 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, you know, 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 archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build 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 wanna 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 that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube 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 timestamp 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. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB 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 in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex 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 influx data.com/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, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, 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, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is 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 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, 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, it's, 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. Mm-hmm <affirmative> 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 Avan 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, is that there's like a hybrid nature to all of these applications where there's 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. Um, 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, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, 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 compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, 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, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx 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. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, 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 InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, 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, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open 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 edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T 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 influx DB. These are companies like ke wear and high bite 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, um, you know, 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 customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, 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 like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So 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 pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, 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 InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. 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. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, 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, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at 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 wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. 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 influx DB system architect. You 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. Yep. The edge is there. Yep. 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, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, 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 downsample 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 detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>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. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna 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 gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, 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, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, 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 gonna talk to two of them, uh, 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. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, 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 new thinking is hyper scale's 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. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, 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 gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view 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 B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, 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 you know, we, 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, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like 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 and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, 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 build, we have the sort of client libraries that allow them to just port to us, you know, 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 gonna 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, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex 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 influence. My name is Aaron Seley. I'm the CTO at HBI. 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 to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the 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 storm 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's 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 highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P 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 the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT 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 is 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 Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna 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, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, 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, you know, have a big software teams, uh, and then eventually worry about, you know, 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, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were 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, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, 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 the, 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, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, 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, you know, you could, 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 of the Caleb just gave you, I mean, 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, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, 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 B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna 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 Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, 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. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, 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. Uh, 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 is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower 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, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through 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, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, 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, what does that mean to you? >>Yeah, so, you know, 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, you know, a lot of kind of slow researching, 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. Um, but to give another practical example, uh, 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. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, 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. Uh, so we have kind of a production monitoring, uh, 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, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, 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. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, 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, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, 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 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, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, 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 field 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 of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, 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 formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and 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, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And 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, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, 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, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So 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 having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, 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 influx DB made possible by influx data. I hope you learn 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 wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, 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 and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

>>from New York. It's the Q covering escape. 19. Hey, welcome back to the Cube coverage here in New York City for the first inaugural multi cloud conference called Escape. We're in New York City. Was staying in New York, were not escapee from New York were in New York. So about Multi Cloud. And we're here. Lisa Marie Nancy, developer advocate for report works, and Eric Conn, vice president of products. Welcome back with you. >>Thank you, John. >>Good to see you guys. So whenever the first inaugural of anything, we want to get into it and find out why. Multiplied certainly been kicked around. People have multiple clouds, but is there really multi clouding going on? So this seems to be the theme here about setting the foundation, architecture and data to kind of consistent themes. What's your guys take? Eric, What's your take on this multi cloud trend? >>Yeah, I think it's something we've all been actively watching for a couple years, and suddenly it is becoming the thing right? So every we just had a customer event back in Europe last week, and every customer there is already running multi cloud. It's always something on their consideration. So there's definitely it's not just a discussion topic. It's now becoming a practical reality. So this event's been perfect because it's both the sense of what are people doing, What are they trying to achieve and also the business sense. So it's definitely something that is not necessarily mainstream, but it's becoming much more how they're thinking about building all their applications Going forward. >>You know, you have almost two camps in the world to get your thoughts on this guy's because like you have a cloud native people that are cloud needed, they love it. They're born in the cloud that get it. Everything's bringing along. The developers are on micro service's They're agile train with their own micro service is when you got the hybrid. I t trying to be hybrid developer, right? So you kind of have to markets coming together. So to me, Essie multi Cloud as a combination of old legacy Data Center types of I t with cloud native not just optioned. It was all about trying to build developer teams inside enterprises. This seems to be a big trend, and multi cloud fits into them because now the reality is that I got azure, I got Amazon. Well, let's take a step back and think about the architecture. What's the foundation? So that to me, is more my opinion. But I want to get your thoughts and reactions that because if it's true, that means some new thinking has to come around around. What's the architecture, What we're trying to do? What's the workloads behavior outcome look like? What's the workflow? So there's a whole nother set of conversations. >>Yeah, that happened. I agree. I think the thing that the fight out there right now that we want to make mainstream is that it's a platform choice, and that's the best way to go forward. So it's still an active debate. But the idea could be I want to do multi club, but I'm gonna lock myself into the Cloud Service is if that's the intent or that's the design architecture pattern. You're really not gonna achieve the goals we all set out to do right, So in some ways we have to design ourselves or have the architecture that will let us achieve the business schools that were really going for and that really means from our perspective or from a port Works perspective. There's a platform team. That platform team should run all the applications and do so in a multi cloud first design pattern. And so from that perspective, that's what we're doing from a data plane perspective. And that's what we do with Kubernetes etcetera. So from that idea going forward, what we're seeing is that customers do want to build a platform team, have that as the architecture pattern, and that's what we think is going to be the winning strategy. >>Thank you. Also, when you have the death definition of cod, you have to incorporate, just like with hybrid a teeny the legacy applications. And we saw that you throughout the years those crucial applications, as we call them. People don't always want them to refer to his legacy. But those are crucial applications, and our customers were definitely thinking about how we're gonna run those and where is the right places it on Prem. We're seeing that a lot, too. So I think when we talk about multi cloud, we also talk about what what is in your legacy? What is your name? I mean, I >>like you use legacy. I think it's a great word because I think it really nail the coffin of that old way because remember, if you think about some of the large enterprises these legacy applications didn't optimized for harden optimize their full stack builds up from the ground up. So they're cool. They're running stuff, but it doesn't translate to see a new platform design point. So how do you continue? This is a great fit for that, cos obviously is the answer. You guys see that? Well, okay, I can keep that and still get this design point. So I guess what I want to ask you guys, as you guys are digging into some of the customer facing conversations, what are they talking about? The day talking about? The platform? Specifically? Certainly on the security side, we're seeing everyone running away from buying tools were thinking about platform. What's the conversation like on the outside >>before your way? Did a talk are multiplied for real talk at Barcelona. Q. Khan put your X three on son. Andrew named it for reals of busy, but we really wanted to talk about multiplied in the real world. And when we said show of hands in Barcelona, who's running multi pod. It was very, very few. And this was in, what, five months? Four months ago? Whereas maybe our customers are just really super advanced because of our 100 plus customers. At four words, we Eric is right. A lot of them are already running multi cloud or if not their plan, in the planning stage right now. So even in the last +56 months, this has become a reality. And we're big fans of your vanities. I don't know if you know, Eric was the first product manager for Pernetti. T o k. He's too shy to say it on dhe. So yeah, and we think, you know, And when it does seem to be the answer to making all they caught a reality right now. >>Well, I want to get back into G k e. And Cooper was very notable historical. So congratulations. But your point about multi cloud is interesting because, you know, having multiple clouds means things, right? So, for instance, if I upgrade to office 3 65 and I killed my exchange server, I'm essentially running azure by their definition. If I'm building a stack I need of us, I'm a Navy best customer. Let's just say I want to do some tensorflow or play with big table. Are spanner on Google now? I have three clouds. No, they're not saying they have worked low specific objectives. I am totally no problem. I see that all the progressive customers, some legacy. I need to be people like maybe they put their tone a file. But anyone doing meaningful cloud probably has multiple clouds, but that's workload driven when you get into tying them together. It's interesting. I think that's where I think you guys have a great opportunity in this community because it open source convene the gateway to minimize the locket. What locket? I mean, like locking the surprise respect if its value, their great use it. But if I want to move my data out of the Amazon, >>you brought up so many good points. So let me go through a few and Lisa jumping. I feel like locking. People don't wanna be locked in at the infrastructure level. So, like you said, if there's value at the higher levels of Stack and it helps me do my business faster, that's an okay thing to exchange. But if it's just locked in and it's not doing anything. They're that's not equal exchange, right? So there's definitely a move from infrastructure up the platform. So locking in infrastructure is what people are trying to move away from. From what we see from the perspective of legacy, there is a lot of things happening in industry that's pretty exciting. How legacy will also start to run in containers, and I'm sure you've seen that. But containers being the basis you could run a BM as well. And so that will mean a lot for in terms of how VM skin start to be matched by orchestrators like kubernetes. So that is another movement for legacy, and I wanted to acknowledge that point now, in terms of the patterns, there are definitely applications, like a hybrid pattern where connect the car has to upload all its data once it docks into its location and move it to the data center. So there are patterns where the workflow does move the ups are the application data between on Prem into a public cloud, for instance, and then coming back from that your trip with Lisa. There is also examples where regulations require companies to enterprise is to be able to move to another cloud in a reasonable time frame. So there's definitely a notion of Multi Cloud is both an architectural design pattern. But it's also a sourcing strategy and that sourcing strategies Maura regulation type o. R in terms of not being locked in. And that's where I'm saying it's all those things. >>You love to get your thoughts on this because I like where you're going with this because it kind of takes it to a level of Okay, standardization kubernetes nights containing one does that. But then you're something about FBI gateways, for instance. Right? So if I'm a car, have five different gig weighs on my device devices or I have multiple vendors dealing with control playing data that could be problematic. I gotta do something. So I started envisioned. I just made that this case up. But my point is, is that you need some standards. So on the A p I side was seeing some trends there once saying, Okay, here's my stuff. I'll just pass Paramus with FBI, you know, state and stateless are two dynamics. What do you make of that? What? What what has to happen next to get to that next level of happiness and goodness because Ruben is has got it, got it there, >>right? I feel like next level. I feel like in Lisa. Please jump. And I feel like from automation perspective, Kubernetes has done that from a P I gateway. And what has to happen next. There's still a lot of easy use that isn't solved right. There's probably tons of opportunities out there to build a much better user experience, both from operations point of view and from what I'm trying to do is an intense because what people aren't gonna automate right now is the intent to automate a lot of the infrastructure manual tasks, and that's goodness. But from how I docked my application, how the application did, it gets moved. We're still at the point of making policy driven, easy to use, and I think there's a lot of opportunities for everyone to get better there. >>That's like Logan is priority looking fruity manual stuff >>and communities was really good at the food. That's a really use case that you brought up really. People were looking at the data now, and when you're talking about persistent mean Cooney's is great for stateless, but for St Paul's really crucial data. So that's where we really come in. And a number of other companies in the cloud native storage ecosystem come in and have really fought through this problem and that data management problem. That's where this platform that Aaron was talking about >>We'll get to that state problem. Talk about your company. I wanna get back Thio, Google Days, um, many war stories around kubernetes. We'll have the same fate as map reduce. You know, the debates internally and Google. What do we do with it? You guys made a good call. Congratulations doing that. What was it like to be early on? Because you already had large scale. You already had. Borg already had all these things in place. Was it like there was >>a few things I'll say One is. It was intense, right? It was intense in the sense that amazing amount of intelligence, amazing amount of intent, and right back then a lot of things were still undecided, right? We're still looking at how containers are package. We're still looking at how infrastructure Kate run and a lot of the service's were still being rolled out. So what it really meant is howto build something that people want to build, something that people want to run with you and how to build an ecosystem community. A lot of that the community got was done very well, right? You have to give credit to things like the Sig. A lot of things like how people like advocates like Lisa had gone out and made it part of what they're doing. And that's important, right? Every ecosystem needs to have those advocates, and that's what's going well, a cz ah flip side. I think there's a lot of things where way always look back, in which we could have done a few things differently. But that's a different story for different >>will. Come back and get in the studio fellow that I gotta ask you now that you're outside. Google was a culture shock. Oh my God. People actually provisioning software. Yeah, I was in a data center. Cultures. There's a little >>bit of culture shock. One thing is, and the funny thing is coming full circle in communities now, is that the idea of an application, right? The idea of what is an application eyes something that feels very comfortable to a lot of legacy traditional. I wanna use traditional applications, but the moment you're you've spent so much time incriminates and you say, What's the application? It became a very hard thing, and I used to have a lot of academic debates wise saying there is no application. It's it's a soup of resources and such. So that was a hard thing. But funny thing is covered, as is now coming out with definitions around application, and Microsoft announced a few things in that area to so there are things that are coming full circle, but that just shows how the movement has changed and how things are becoming in some ways meeting each other halfway. >>Talk about the company. What you guys are doing. Taking moments explaining contacts. Multi Cloud were here. Put worse. What's the platform? It's a product. What's the value proposition? What's the state of the company? >>Yes. So the companies? Uh well, well, it's grown from early days when Lisa and I joined where we're probably a handful now. We're in four or five cities. Geography is over 100 people over 150 customers and there. It's been a lot of enterprises that are saying, like, How do I take this pattern? Doing containers and micro service is, and how do I run it with my mission? Critical business crinkle workloads And at that point, there is no mission critical business critical workload that isn't stable so suddenly they're trying to say, How do I run These applications and containers and data have different life cycles. So what they're really looking for is a data plane that works with the control planes and how controlled planes are changing the behavior. So a lot of our technology and a lot of our product innovation has been around both the data plane but a storage control plane that integrates with a computer controlled plane. So I know we like to talk about one control plane. There's actually multiple control planes, and you mentioned security, right? If I look at how applications are running way, acting now securely access for applications and it's no longer have access to the data. Before I get to use it, you have to now start to do things like J W. T. Or much higher level bear tokens to say I know how to access this application for this life cycle for this use case and get that kind of resiliency. So it's really around having that >>storage. More complexity, absolutely needing abstraction layers and you compute. Luckily, work there. But you gotta have software to do it >>from a poor box perspective. Our products entirely software right down loans and runs using kubernetes. And so the point here is we make remarries able to run all the staple workloads out of the box using the same comment control plane, which is communities. So that's the experiences that we really want to make it so that Dev Ops teams can run anywhere close. And that's that's in some ways been part of the mix. >>Lisa, we've been covering Jeff up. Go back to 2010. Remember when I first I was hanging around? San Francisco? Doesn't eight Joint was coming out the woodwork and all that early days. You look at the journey of how infrastructures code. We'll talk about that in 2008 and now we'll get 11 years later. Look at the advancements you've been through this now the tipping point just seems like this wave is big and people are on developers air getting it. It's a modern renaissance of application developers, and the enterprise it's happening in the enterprise is not just like the energy. You're one Apple geeks or the foundation. It's happening in >>everyone's on board this time, and you and I have been in the trenches in the early stages of many open source projects. And I think with kubernetes Arab reference of community earlier, I'm super proud to be running the world's largest CNC F for user group. And it's a great community, a diverse community, super smart people. One of my favorite things about working poor works is we have some really smart engineers that have figured out what companies want, how to solve problems, and then we'll go credible open source projects. We created a project called autopilot, really largely because one of our customers, every who's in the G s space and who's running just incredible application, you can google it and see what the work they're doing. It's all out there publicly. Onda we built, you know, we've built an open source project for them to help them get the most out of kubernetes we can say so there's a lot of people in the community system doing that. How can we make communities better? Half We make competitive enterprise grade and not take years to do that. Like some of the other open source projects that we worked on, it took. So it's a super exciting time to be here, >>and open source is growing so fast. Now just think about having project being structured. More and more projects are coming online and user profit a lot more. Vendor driven projects, too used mostly and used with. Now you have a lot of support vendors who are users, so the line is blurring between then their user in open source is really fast. >>Will you look at the look of the landscape on the C N. C. F? You know the website. I mean, it's what 400 that are already on board. It's really important. >>They don't have enough speaking slasher with >>right. I know, and it's just it. It is users and vendors. Everybody's in the community together. It's one of things that makes it super exciting, and it's how we know this is This was the right choice for us. Did they communities because that's what? Everybody? >>You guys are practically neighbors. We look for CNN Studio, Palo Alto. I wanna ask you one final question on the product side. Road map. What you guys thinking As Kubernetes goes, the next level state, a lot of micro service is observe. Ability is becoming a key part of it. The automation configuration management things are developing fast. State. What's the road for you guys? For >>us, it's been always about howto handle the mission critical and make that application run seamlessly. And then now we've done a lot of portability. So disaster recovery is one of the biggest things for us is that customers are saying, How do I do a hybrid pattern back to your earlier question of running on Prem and in Public Cloud and do a D. R fail over into a Some of the things, at least, is pointing out. That we're announcing soon is non Terry's autopilot in the idea of automatically managing applications scale from a volume capacity. And then we're actually going to start moving a lot more into some of what you do with data after the life cycle in terms of backup and retention. So those are the things that everyone's been pushing us, and the customers are all asking, >>You know, I think data that recovery is interesting. I think that's going to change radically. And I think we look at the trend of how yeah, data backup recovery was built. It was built because of disruption of business, floods, our games. That's right. It is in their failure. But I think the biggest disruptions ransomware that malware. So security is now a active disruptor, So it's not like it After today. If we hadn't have ah, fire, we can always roll back. So you're infected and you're just rolling back infected code. That's a ransomware dream. That's what's going on. So I think data protection needs to redefine. >>What do you think? Absolutely. I think there's a notion of how do I get last week's data last month and then oftentimes customers will say If I have a piece of data volume and I suddenly have to delete it, I still need to have some record of that action for a long time, right? So those are the kinds of things that are happening and his crew bearnaise and everything, it gets changed. Suddenly, the important part is not what was just that one pot it becomes. How do I reconstruct everything? Action >>is not one thing. It's everywhere That's right, protected all through the platform. It is a platform decision. It's not some cattlemen on the side. >>You can't be a single lap. It has to be entire solution. And it has to handle things like, Where do you come from? Where is it allowed to go? >>You guys have that philosophy? >>We absolutely. And it's based on the enterprises that are adopting port works and saying, Hey, this is my romance. I'm basing it on Kubernetes here, my data partner. How do you make it happen? >>This speaks to your point of why the enterprise is in the vendors jumped in. This is what people care about security. How do you solve this last mile problem? Storage, Networking. How do you plug those holes and kubernetes? Because that is crucial. >>One personal private moment. Victory moment for me personally, Waas been a big fan of Cuban, is actually, you know, for years in there when it was created, talked about one of moments that got me was personal. Heartfelt moment was enterprise buyer on. The whole mindset in the enterprise has always been You gotta kill the old to bring in the new. And so there's always been that tension of a you know, the shame, your toy from Silicon Valley or whatever. You know, I'm not gonna just trash this and have a migration is a pain in the butt fried. You don't want that to do that. They hate doing migrations, but with containers and kubernetes, they actually they don't end of life to bring in the new project they could do on their own or keep it around. So that took a lot of air out of the tension in on the I t. Side. Because it's a great I can deal with the life cycle of my app on my own terms and go play with Cloud native and said to me, I was like, That was to be like, Okay, there it is. That was validation. That means this is real because now they will be without compromising. >>I think so. And I think some of that has been how the ecosystems embraced it, right, So now it's becoming all the vendors are saying My internal stack is also based on company. So even if you as an application owner or not realizing it, you're gonna take a B M next year and you're gonna run it and it's gonna be back by something like >>the submarine and the aircon. Thank you for coming on court. Worse Hot started Multiple cities Kubernetes Big developer Project Open Source Talking about multi cloud here at the inaugural Multi Cloud Conference in New York City Secu Courage of Escape Plan 19 John Corey Thanks for watching.

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners okay welcome back and we're live here in Las Vegas day three last interview of the day three days of wall-to-wall coverage two sets here at AWS reinvent 2018 our sixth year we've been at every reinvent except for the first one and it's been great to watch the rise I'm Jeff we're with Dean Volante we're here with Andy Jesse the CEO of AWS started this as it working backwards document years ago twelve years ago 12 half year zine years ago was when the document was written and we've launched 12 and a half years ago great to see you thanks for spending time I know you're super busy congratulations we met last week you couldn't really talk about it but boy there was so much more payload in the announcements than they were before are you happy with the results certainly three our keynote was taxing what's good impression when the keynote was over but ya know we're thrilled with it and most importantly the reason we're thrilled is because our customers are thrilled I think they just couldn't believe how much we delivered this week you know well over 100 capabilities and they were super excited about you know the storage announced was the thing is when you have millions of customers any announcement you make is going to be popular with thousands of customers so some people walked up to me and said oh I know it's not sexy but I love the storage announcements I needed the file systems I wanted that glacier deep archive some customers love the database releases with lots of customers that were excited about the machine learning piece and you know the another unsexy one where the enterprise abstractions just to make it so much easier for that type of builder who wants more prescriptive guidance to be able to get started quicker and then you know people are pretty excited that outpost too so it has you a question I'll talk Amazon speak now what what areas of the show do you feel you raise the bar this year on what was that what would you point to bar raising moments announcements well you know I think each year one of the things I like about reinvent and that we work hard on is we'd like to have we don't really want it to be a corporate event we wanted to be quirky and we want it to be authentic and we want you know we want our community to fit to have fun here while they also learn so you know Midnight Madness is for instance something we do every you know we've done the last couple years and we try radically different things and so I thought that Tatanka eating contest raised the bar is again this year was the second year in a row that we said again as political World Records and you know I thought I really liked Peter De Santis --is and Myrna Vogel's keynotes on Monday and today respectively I thought they both were fantastic and you know keep raising the bar are you over a year and you know so they're you know we're hoping I too will be something that people feel like raise the bar year over year what the house band synchronicity was quite good too you know yeah I tell you that that fan is terrific and you know and I think that again all those things I mentioned are part of what we you know think makes the event fun and quirky and different but the most important thing by far is the learning of the education and then our customers excited about not just the platform but we launched so many things do they feel like it helps them do their job better well while we're on the raising bar we've got a prop here this is the the deep racer deep the deep racer machine learning it's a toy for testing and the question comes up how old do you have to be to use this and I said hey if your kid can code machine learning good for them but talk about this because this is kind of interesting because it's fun but where'd this come from you know it came from last year when we release sage maker and we were making machine learning so much easier for everyday developers of scientists we said what can we do to give people hands-on experience because you learn things better if you actually try it and so we tried to help developers get more experience to computer vision by having a deep lens you know video camera and that was wildly popular and so as we were thinking about this year making reinforcement learning available as easily as we are in sage maker which we think is a huge potential game-changer grant Forsman learning the team kept thinking about it's great but nobody knows enforcement learning and nobody has experienced with it how can we give them experience what are ways they can get hands-on experience and that's how the deep racer car came up which is really making it simple where they can just give us a reward function with a line of Python strip and then Sage Maker will automatically train an RL algorithm and then they get to play it to the car and then race against one another and when we watched how competitive it was getting inside our own house on these RL infused cars racing each other we figured other people might find a compelling as well and I couldn't believe how many people participate yesterday yeah and then I don't know if you saw it three burners right before burners keynote the finals were really exciting to like the fact that there were some imperfections were actually made it more compelling to watch and so we had a racer Cup coming up - I met play 19 competitive yes that's going to happen yeah today was the accelerated version of the first ever deep racer League championship Cup but next year will be a full season at our 20 AWS summits the top winners in the in the deep racers you see bracer League races at each of those summits plus the top 10 vote getters in points from those summits will come here and compete for the championship Cup now you and I talked about a new persona last week when we met but now the announcements pretty clear now why this points to a whole new persona developer you got eSports on the twitch side booming heat sports is changing the game and in the whole digital sports category robotics space you got a satellite announcement this is a genre changed in digital culture and you see the AI stuff and machine learning how does the web services stack play in this new world where AI is now a service it's a whole nother paradigm shift what's your thoughts on all that well you know I mean all those areas that were continuing to expand into our areas that our customers are asking us to help them with and where there are huge opportunities for customers but where it's hard I mean if you look at space as an example if you've to interact with a satellite it's it's expensive to have to have all those satellites set up you know and those drown ground antennas set up and then you have to program them and then and you actually have to pay this fixed price instead of on-demand customers so why can't you give us access those satellites the way we consume AWS and then if you can have the ground antennas where when the data comes down from the satellite it's basically on the same premises as your AWS region so we can store the data and process the data analyze it and take action that is very compelling so that that just felt like a natural fit you know and the same thing with robotics I think that robotics is one of the most underrated areas of Technology I think robots will do all kinds of things for us at work and in a home and the tools out there to make it easy to build robotic applications and to do the simulation to deploy them and then have them work with the rest of your applications and infrastructure have been pretty primitive and so robot maker is I agree with you I think you look at the younger generation too even at the high school elementary level people are gravitating towards robotics robotics clubs are booming that maker culture goes through a whole nother level with robotic congratulation you know it's funny we had the youngest person to ever pass the AWS certification exam is a kid named Karthik nine years old passed and he was here this week actually and I got a chance to meet with him today and I said well after the certification what are you doing he said well I'm building a robot you know I'm feeling Ruben he said now with your launch of deep racer I want to try and find a way to to have the deep racer car be the eyes and the camera and the reinforcement learning for my robot nine years old yes it's gonna be a different generation with what they build John and I were talking this morning Andy at our open about you're making it harder for the critics used to be self-service only it criticized your open source contributions the hybrid strategy your turn a tick in the box is on all those outpost was I think surprised a lot of people it didn't so much surprise us that you were moving in that direction but I wonder if you could sort of talk about some of those key initiatives I know it's customer driven but wow the the TAM expansion the the customer value that you're bringing it's like a whole new era that you're entering yeah you know everything we build is you guys know we talk about all the time it's just it's driven by what customers want and so we just started over the last six months you know and really by virtue of having this partnership with VMware where we have a lot of enterprise engagement as they're moving to the cloud using VMware cloud and AWS we had a bunch of customers say it's really great I'm moving most my application of the cloud but there's some that aren't moving for a while because they got to be close to selling on-premises and I want to use AWS for this I don't want a different environment can you just find a way to put some services like compute and storage on-premises and hardware but I want to actually use the same control plan I'm going to use for the rest of AWS and I wanted to easily connect with the rest of my applications in AWS and we had you know we didn't like as you and I talked about a week or two ago we just have not like the model that's been out there so far to do this because it's you know the control plane is different the api's are different the tools are different the hardware is different the functionality is different and customers don't like it's why it's not getting much traction and we didn't want to pursue it if we didn't think it was going to be useful but we had this concept we were working on with a couple customers where they wanted compute and storage on-premises but they wanted to have that connect with all the other applications in the AWS cloud and so we have this idea that maybe this local set of compute and storage would be like a far zone from an availability zone they were using and we started thinking about that and we thought there was much more generalized idea which became outposts and so the thing that I think people are gonna love about that is for the applications that can't move easily because they need to be close slang on-premises you get AWS like real AWS compute real AWS Storage Analytics database sage maker will be in there as well but it's the same api's same control playing the same tools the same hardware we use in our data centers and it will easily connect through the same control plane to the rest of AWS the rest of the services and the rest of their applications there so and it provides a platform for a whole host of new services down I mean every customer meeting I've had in the last we made the announcement people are excited about I want to ask you guys are talking about all the innovation and new areas and we're seeing an expansion of the AWS distinct brand and things like TV advertising statcast I wonder what's behind that can you address that yeah it's a good question I mean there's kind of two different types of I'll call it TV advert Swartz we're doing one is straight-up advertising one is less so which is you know the one that less so is that a number of the sports leagues are really interested in and actually pretty sophisticated in using cloud computing and analytics and machine learning if you look at Major League Baseball now NFL and Formula One and they want to make the user experience and the viewer experience so much better and so they're building on top of AWS and then we like the ability of helping them showcase the capabilities that they're you know both the customer experience and the ml and AI capabilities then there's just a straight-up advertising them that we've been trying we tried a little bit of it last q4 and you know it's always very difficult to quantitatively measure tvf but we have a lot of ways that we try to triangulate that and we were really surprised and what looked like the positive numbers we saw for both TV as well as the outdoor media and things like in the airports and things like that and so we decided we would try it again this q4 and you know I think I would call us right now still experimenting yeah and it's very much kind of what Amazon does which is we try different things to see what resonates the see Whitefield says so so far so good and we expect to keep experimenting I I think that's a good call because the brand lift is probably there I'll see impressions get reach vehicle but you guys are in a rising tide market we're hearing co-creation VMware co-creating deep meaningful partnerships you always talk about that so it's kind of this success model of innovation to reimagine the satellite Lockheed Martin a partnership this seems to be a new way to do business in this rising tide how are you guys getting the word out education people want to know more this is a big kind of movement yeah well you know I think that if you looked at the first several years of AWS I was always surprised when I would go see enterprises and they would have no idea that Amazon was doing anything in the cloud even though we had the only cloud offering at the time so I think if you compare where we were a few years ago to today there's you know gigantic awareness relatively speaking but I still think that there are so many majority of workloads still live on premises I mean we have a twenty seven billion dollar revenue run rate business it's growing forty six percent year-over-year and yet we're still at the early stages of the meet of enterprise of public sector adoption in the u.s. you go outside the US where there twelve to thirty six months behind depending on the country in the industry and sometimes it feels like you know like Groundhog's Day well you guys are doing regions out there Italy as was announced yeah you're expanding very fast globally can you talk about that real quick yeah it's it's a you know we've had customers from 192 countries using AWS for many many years but they've been using AWS in regions outside of their country usually because there are a lot of workloads that could stand that latency and where the data doesn't have to be on natural soil but increasingly if you want to help customers get done what they want to and serve the broader array of their applications you have to have regions in their country both so that they have lower latency to their end users and because the data sovereignty laws which are getting really more rigid rather than more flexible let me ask you a question about competition you you said I can't members on the cube or in person there's no compression reach out gorilla for experience and time elastic economies with scale when you have copycat people trying to copy Amazon how do you talk about some of those things that are those diseconomies of scale what are the points that customers should look at when they say okay I got someone else is talking cloud Amazon's got years of experience ahead of the competition more services what do you talk about what do you point to you it's not about slimming the competition but what is the diseconomy of scale to try to match the trajectory of Amazon yeah it's it's a bunch of things you know first of all it's operational performance you know a lot of the hardest lessons you learn and operating of scale only happen when you get to that level of scale and you know there's some events that we see sometimes elsewhere we look at that and then we read the post-mortem we say oh yeah 2011 you know we remember they went through that I don't wish it on anybody but when you have a business at several times larger than the next or providers combined you just said a different level of scale and you've learned lessons earlier I also think that the reason that we continue to have both so much more functionality and innovate at a faster clip and seem to get capabilities that customers want is because we have so many more customers than anybody else you know a lot of times and this is happening all week to where customers will say to me I can't believe that you knew that I wanted that and I always say it's because you told us yeah it's not like we're Nostradamus you've told us that and so when you have so many more customers and when they feel free to give you feedback and when you've built good mechanisms like we have to get that feedback from the field to the product builders it means there's this real flywheel of getting you know getting more customers leads to more feedback leads to more features leads to better functionality where there's a network effect from being on the platform with all those other customers and all those industries I wonder if you could add some color to a premise that we've put forth on your edge strategy so what you guys you know we do a lot of these shows and a lot of the IOT and edge strategies that we've seen from traditional IT players what you call the old guard have fallen flat in our opinion because it's a top-down approach it reminds us of the Windows Phone it just didn't work and it's not going to work as their operations technologies people we see what you've announced here as a Bottoms Up approach you developing an application platform to build secure and manage apps for those folks right at the edge I wonder if you could add some color to that and some thoughts on your edge strategy yeah I mean again for us if we don't have some top-down strategy that you know that I think is grandiose it's just what customers want and so we have so many customers who have all these devices at the edge and all these assets at the edge and they said to us well the first problem I have I want to get this data into the cloud and then I want to do analytics item we say ok well how can we help they say well the first thing is I don't even know how to translate this data from the device protocol to just being able to operate in the cloud so that's the first problem we go solve well then people say ok now I can get it in but I actually I need security like you know if you look at the amount of security options for these edge devices it's a new field you know let that dine attack that took a lot of the internet down a couple years ago came from you know a device on the edge and so that's why you know we built you know a security capability and people say well okay now you've made it so I can run devices but if I'm gonna run thousands of devices I need a way to manage all those devices of scale and we build telling to manage two devices and people say well ok it's great that I can do it and device is big enough that have a CPU but what about when they don't have a CPU you know they have just a microcontroller and that's why we built the our toss piece and you know the list kind of keeps going people so this is great now that I get all this data in the cloud I can take all these analytics actions but on my device sometimes I don't want to make the round trip to the cloud so can you give me a way to use the same programming model and and pick which triggers I want to take action with cloud versus those that want to take on the device itself which was what green grass was so all of those pieces is not some kind of top-down master plan as much as we know that customers have all these devices the edge that they want to use that data analyze that data take action on that data and send it back in multiple ways and you have you have the cloud platform to give them the services to make the tools the right tools for the right job yeah that's the main team yeah so I got to ask you about one of the big controversies that we don't think that's that controversial but the chips that you announced new Amazon Web Services front microprocessors the chips yeah do two of them talk about them and Intel's also a partner a lot of people are talking about this in the press yeah Intel Amazon chips well that annapurna acquisition is Norton they bear fruit was 2015 I think yeah early it really the annapurna team is fantastic and they've added a huge amount of value to AWS and Amazon as a whole you know the first thing I would say is that Intel is a very deep partner of AWS and will be for a long time I mean that that's not changing and we've been a long thought that they were gonna be lots of different processors out there and and different ones that did different things at different price points and so like a lot of other companies we've been interested in arm for a long time and for a while it wasn't mature enough and the technology is matured and we found a way in in building our own ARM chip with graviton where we think we can allow customers to run a lot of their scale out generalize were close but up to 45 percent less expensively and so when you find a value proposition that compelling for customers you need to do it and you know as I mentioned in the keynote yesterday when we were talking about inference we feel like a lot of the world has been solvent for training and not solvent as much for inference yet and we've made training so much easier with the things that we've built in AWS over the last couple years but inferences where most of the cost is gonna be and so elastic inference we think it you know will allow people to be much more efficient in how they use them for use and how they spend money but when you've got the type of workloads at scale and productions that use whole GPUs or that need that low latency where you need it on the hardware of a chip that's optimized for inference they is faster that's more cost effective that's high throughput we can get hundreds of tops on it and thousands to you ban them together he's gonna totally change the game for imprison and so that was something that wasn't easy for us to find elsewhere and when we have team fortunately they could build it and it's the combination of the elastic service of inference with the chip that makes the difference it specialism there so it's not like I mean you can use each on their own and we expect they'll be a bunch of customers who will use each on their own but there will be an opportunity to use those in combination that will be very powerful it comes down to really deeply understanding the customer problem again at night training versus inference and everybody talks about the training right the the technical challenge you got a child is the internet and tells gonna make a lot of money as it stands expanding market banding so they'll get their share the chips get taped out their con a couple year to three year life cycles and everything starts anew every time somebody's building a new chip so I think it's actually great for customers of all sorts that there's multiple processors that are possible but we will have a deep relationship with Intel forever I think so I want to talk about one of the cool demos you did on stage not a lot you did customer did f1 that was a super cool I love that imagery because it said an analogy of high performance competitive racing that can be applied to this play sports anything and the level of accuracy that they need in the real time time series kind of encapsulates a lot of the cloud value talk about the f1 analytic thing are you guys gonna sponsor these events there's a relationship there give us what the picture of what's going on there you have a deep relationship with Formula One where they're using our platform to to do their all their digital properties as well as their analytics and machine learning and it was super cool to see Ross demo the way that they're changing the user experience for for viewers and you know it's it's it's an amazing sport you know it's not watched as much maybe in the US but outside the US that is the motorsport and the way that they're changing the experience the way that they're able to assess what's happening with drivers and with cars and then predict what's actually happening and make the viewer feel like they're actually either in the cockpit or actually in the pit itself with it with the crew is it's really exciting and it's non err to be a partner so you do some events they'll get the cube they're these these big time again there's a tech angle now and everything it's a plug for you to be at the they have one event cloud demócrata you're hitting now new industries I mean this is the thing right I mean it's disrupting every industry I mean what aren't you disrupting I mean what areas do you see that yet aren't coming online to the cloud I don't see industry segments at this point that aren't moving to the cloud I would have told you 18 to 24 months ago that I felt like financial services was moving a lot more slowly than then I thought they should or you know probably healthcare also was a little bit slower but both of those industry segments are moving very aggressively well it's taking longer they're high-risk industries and the digital transformation has it occurred fast enough but it's coming and there's regulatory pieces that they legitimately have to sort through and you know we have just if you look at financial services as an example we have a pretty significant team that does nothing but work with our partners to help them with the regulatory bodies because what we find is when we go with a customer to a regulator and show them a real use case and then how it will be done in a DOP is the regulator says oh well that's more secure that you do on-premises and so it's just an education process and you know I think that's been helpful in it and I'll get final questions for you what have you observed here at reinvent Houston glad people talking so you get a lot of feedback actually to clopped two-part question because I was asked the final final question so I'll just get it out front what are people missing of all the announcements you've had a lot of signal in there a lot of a lot of announcements what are what is something that you've observed that you think should be amplified that people might have not overlooked but like you feel like it's more important to sign the light on we'll start with that one well you know it's a little hard for me to tell this moment just because there have been so many in such a short amount of time and and if we just look a little bit at the coverage it seems and if I take just as inputs they comments and and the questions from customers it's been pretty broadly understood and people are pretty excited and as I said different segments have kind of their favorite areas but I feel like people are pretty excited by the breadth of capabilities you know I think that if I pick two in particular I would say that people are still in the machine learning space people are blown away by how much we provided are all three layers of the stack I think people are still getting their heads around which layer of the stack am I gonna participate at you know I mean the one that probably has the most potential for most companies is that middle layer because most companies have gobs of data and there are jewels in that data and if you can enable their developers their everyday developers to be able to build models and get at the predictive value and add value that has huge impact for companies moving forward but most modern companies with technology functions will use all three layers of the stack and so just getting their arms around which layers of the stack they should take advantage of first and having the personnel to be able to do it and we're making that much easier with things like sage maker and then you know I think if you look at the blockchain space I think that that is just one of those spaces that has a huge amount of buzz people talk a lot about it exactly sure sometimes what they're gonna do but but I also think that a lot of people said to us that breaking those into those two real customer jobs to be done and then having a great solution that does each of those jobs really well is not only something that AWS does all the time that makes it easier for them but it also made it easier for a lot of them to understand that a lot of customers said to us you know that qld be that ledger database with a single trust of central authority for my supply chain that's what I need for my supply chain I don't need all the complexity of a blockchain framework and then there were a lot of other people said oh yeah that is what I want I wanted to decentralize trust between peers but I just needed a way easier way to manage hyper ledger fabric and etherium so I think those are two that people like are so interested and still figuring out how to use as expansively as I think they hope they will Andy thanks so much for your time and I want to just say watching you guys in the past six years has been a fun journey together but watching the execution you guys have done an amazing job of keeping your eye on the ball and being humble but being proud and loud at the same time so congratulations and you know guns blaring in 2019 what's your top pray all right besides listening to the customers what's your top 20 19 we know you listen to cut oh my gosh we have so many things that we're doing in 2019 but you know we have a lot of delivery in front and in front of us I mean as much as we launched 140 unique things over the last six to eight business days and yet I tell you to stay tuned the rest of 2018 we have more coming and then in nineteen you'll you should expect to see more few capabilities more database capabilities more machine learning capabilities more analytics capability look a lot I could spend all night John we don't need it we don't need a post reinvent post you know traumatic announcements syndrome because just to digest it all yeah it's a lot of work looking forward to seeing how enterprises continue to make to to kind of manage their hybrid approach as they're as they're making this trend transition from on-premises to the cloud how many continue to jump on to VMware cloud an AWS how many jump onto outpost so I think that that transition and helping customers do that easily is something on here of course we'll be commentating and pontificating on that for the next year thanks for your time I really should have me and I appreciate that you guys come at regular pay our pleasure okay winding down that's the last interview here wall to wall covers two cents 110 interviews in the books we'll have 500 video assets total blog post on Sylvia angle calm that's reinvent closing down 2018 thanks for watching [Music]