>>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.

>>Okay. Today we welcome Evan Kaplan, CEO of Influx Data, the company behind Influx DB Welcome, Evan. Thanks for coming on. >>Hey, John. Thanks for having me. >>Great segment here on the influx. DB Story. What is the story? Take us through the history. Why Time series? What's the story? >>So the history of history is actually actually pretty interesting. 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 times 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 have to do a tonne of work just to start doing work. Which means you have 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 optimised 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 y Combinator and he built something for he made his first commit to open source influx TB at the end of 2013. And basically, you know, from my point of view, you invented modern time series, which is you start with a purpose built time series platform to do these kind of work clothes, and you get all the benefits of having something right out of the box or 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 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. Because if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light. They're measuring their instrumented something over time. And so I thought that would be super relevant over long term, and I've not regretted. Oh, >>no, and it's interesting at that time to go back in history. You know the role of databases are relational database, the one database to rule the world. And then, as clouds started coming in, you're starting to see more databases, proliferate types of databases. And Time series in particular, is interesting because real time has become super valuable. From an application standpoint, Iot, which speaks Time series, means something. It's like time matters >>times, >>and sometimes date is not worth it after the time. Sometimes it's worth it. And then you get the Data lake, so you have this whole new evolution. Is this the momentum? What's the momentum? I guess the question is, what's the momentum behind >>what's causing us to grow? So >>the time series. Why is time series in the category momentum? What's the bottom line? We'll >>think about it. You think about it from abroad, abroad, 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 self healing software system. Everybody wants to build increasing intelligence systems, and so, in order to build these increasingly intelligence systems. You have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened? What happened? What happened? What's going to happen? And so you get to these applications, like predictive maintenance or smarter systems. And increasingly, you want to do that stuff not just intelligently, but fast in real time, so millisecond response, so that when you're driving a self driving car and the system realises that you're about to do something, essentially, you want to be able to act in something that looks like real time. All systems want to do that. I want to be more intelligent, and they want to be more real time. So we just happened to, you know, we happen to show up at the right time. In the evolution of the market. >>It's interesting. Near real time isn't good enough when you need real time. Yeah, >>it's not, it's not, and it's like it's like everybody wants even when you don't need it. Uh, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature even though you're not going to use it, you decide that you're buying criteria. Real time is a buying criteria. >>So what you're saying, then is near real time is getting closer to real time as possible as possible. Okay, so talk about the aspect of data cause we're hearing a lot of conversations on the Cubans particular around how people are implementing and actually getting better. So iterating on data. >>But >>you have to know when it happened to get know how to fix it. So this is a big part of what we're seeing with people saying, Hey, you know, I want to make my machine learning albums better after the fact I want to 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 for sure, what you're saying is is none of this is non linear. It's all incremental. And so if you take something, you know, just as an easy example. If you take a self driving car, what you're doing is your instrument in 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. Correct for that in the software, if you do that four billion times, you get a self driving car. But every system moves along that evolution. And so you get the dynamic of you know of constantly instrumented, watching the system behave and do it and this and sets up driving cars. 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. >>What are the top application? What are you seeing your 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 pretty easy to understand. On one side of the equation. That's the physical side is sensors are the 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 they're all on that side. They're all about Iot. So they're talking about consumer Iot projects like Google's Nest Tato Um, particle sensors, Um, even delivery engines like Happy who deliver the interesting part of South America. Like anywhere. There's a physical location doing 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 it has to get smarter when you're building, when you're building a factory, systems all have to get smarter. And then lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid motors, Nicola Motors, um you know, lots to do with electric cars, solar arrays, windmills are raised just anything that's going to get instrumented, that where that instrumentation becomes part of what the purpose is. >>It's interesting. The convergence of physical and digital is happening with the data Iot you mentioned. You know, you think of Iot. Look at the use cases there. It was proprietary OT systems now becoming more I p 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 that? What's the Iot? Where's the Iot dots connecting to? Because, you know, as these two cultures merge operations basically industrial factory car, they gotta get smarter. Intelligent edge is a buzzword, but it has to be more intelligent. Where's the where's the action in all this? So the >>action really, really at the core? >>It's >>at the developer, right, Because you're looking at these things. It's very hard to get off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developers. And so what you're seeing is a movement in the world that that maybe you and I grew up in with I t r o T moving increasingly that developer driven capability. And so all of these Iot systems, their bespoke, they don't come out of the box. And so the developer and the architect, the CTO they define what's my business? What am I trying to do trying to sequence the human genome and figure out when these genes express themselves? Or am I trying to figure out when the next heart rate monitor is going to show up in my apple watch, right? What am I trying to do? What's the system I need to build? And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right, used to be used by an application or a service or a sad 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 death of a 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 test look at applications of the cars right there. I mean, there's the modern application lifecycle now, so take us through how this impacts the developer doesn't impact their CI CD. Pipeline is a cloud native. I mean, where does this all Where does this go to? >>Well, so first of all you talking about, there was an internal journey that we had to go through as a company, which which I think is fascinating for anybody's interested as we went from primarily a monolithic software that was open source to building a cloud native platform, which means we have to move from an agile development environment to a C I C d. Environ. So two degree that you're 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 your race right to the degree that services cloud then increasingly removed from an agile development to a CI CD environment which is shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also going to happen in a big way >>when your customer base that you have now and you see evolving with influx DB is it that they're gonna be writing more of the application or relying more on others? I mean, obviously the open source component here. So when you bring in kind of old way new Way Old Way was, I got a proprietary platform running all this Iot stuff and I got to write, Here's an application. That's general purpose. I have some flexibility, somewhat brittle. Maybe not a lot of robustness to it, but it does its job >>a good way to think about this. >>This is what >>So, yeah, a good way to think about this is what What's the role of the developer slashed architect C T o that chain within a large enterprise or a company. And so, um, the way to think about is I started my career in the aerospace industry, and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts instead. What they do is they assemble, they buy the wings, they buy the engines they assemble. Actually, they don't buy the wings. It's the one thing they buy, the material of the way they build the wings because there's a lot of tech in the wings and they end up being assemblers, smart assemblers of what ends up being a flying aeroplane, which is pretty big deal even now. And so what happens with software people is they have the ability to pull from, you know, the best of the open source world, so they would pull a time series capability from us. Then they would assemble that with potentially some E t l logic from somebody else, or they 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 because you're not writing native code for everything, >>so they're more flexible. They have faster time to market because they're assembling way faster and they get to still maintain their core competency. OK, the wings. In this case, >>they become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who started build stuff. By the way. This is not different than the people have just up the road Google have been doing for years or the tier one Amazon building all their own. >>Well, I think one of the things that's interesting is that this idea of a systems developing a system architecture, I mean systems, uh, systems have consequences when you make changes. So when you have now cloud data centre 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. That's exactly >>that's where that's where the, you know that that Boeing or that aeroplane building analogy comes in for us. We've really been thoughtful about that because I o. T. It's critical. So are open Source Edge has the same API as our cloud native stuff that hasn't enterprise on premises or multiple products have the same API, and they have a relationship with each other. They can talk with each other, so the builder builds at once. And so this is where when you start thinking about the components that people have to use to build these services is that you want to make sure at least that base layer that database layer that those components talk to each other. >>We'll have to ask you. I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>I mean, you have appeal for >>a big check blank check. If you can answer this question only if you get the question right. I got all this important operation stuff. I got my factory. I got my self driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about Time Series? Because now I have to make these architectural decisions as you mentioned and it's going to impact my application development. So huge decision point for your customers. What should I care about the most? What's in it for me? Why is time series important? Yeah, >>that's a great question. So chances are if you've got a business that was 20 years old or 25 years old, you're already thinking about Time series. You probably didn't call it that you built something on a work call or you build something that IBM 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, they're 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 optimising those systems over time. I hate the word but digital transformation, and you start with Time series. It's a foundational base layer for any system that you're going to build. There's no system I can think of where time series shouldn't be the foundational base layer. If you just want to store your data and just leave it there and then maybe look it up every five years, that's fine. That's not time. Serious time series 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 P o for you in a big check, what what's the value to me as like when I implement this What's the end state? What's it look like when it's up and running? What's the value proposition for me? What's in it? >>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 transforming it in near real time. So the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling intelligence system. I think that's what developers and architects are seeing now. >>Bottom line. Final word. What's in it for the customer? What's what's your What's your statement of the customer? Would you say to someone looking to do something in time, series and edge? >>Yeah. So it's pretty clear to clear to us that if you're building, if you view yourself as being in the building business of building systems that you want them to be increasingly intelligent, self healing, autonomous, you want them to operate in real time that you start from Time series. I also want to say What's in it for us in flux? What's in it for us is people are doing some amazing stuff. I highlighted some of the energy stuff, some of the human genome, some of the health care. It's hard not to be proud or feel like. Wow. 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. >>It's critical infrastructure, critical critical operations. >>Yeah, great >>stuff. Evan. Thanks for coming on. Appreciate this segment. All right. In a moment. Brian Gilmore, director of Iot and emerging Technology that influx, they will join me. You're watching the Cube leader in tech coverage. Thanks for watching

>>Welcome to this cube conversation. I'm Lisa Martin learn Dubois joins me next VP of product management at Dell technologies. Laura, welcome back to the program. >>Yeah. Thank you so much, Lisa. It's just fantastic to be here and talking about data protection. Um, you know, now that we're coming out of COVID, it's just wonderful to be here. Thank you so much. >>Isn't it so refreshing. So you're going to provide some updates on Dell's data protection software, some of the innovation, how you're working with customers and prospects on that. So let's go ahead and dig right in. Let's talk about some of the innovation and the enhancements that Dell is making to its data protection suite of software, and also how customers are influencing that. >>Yeah, so it's a great question, Lisa and you're right. We have driven a lot of innovation and enhancements in our data protection suite. And let me just level set a second. So data protection suite, you know, is a solution that is deployed by really tens of thousands of customers. And we continue to innovate and enhance that data protection suite data protection suite is comprised primarily of three main data protection, software capabilities, so longstanding capabilities and customer adoption of Avamar, which continues to be a central capability on our portfolio. The second one is networker. Um, so networker is also an enterprise grade, highly scalable and performance data protection solution. And then a couple of years ago, um, we launched a new data protection capability called power protect data manager. So all three of these capabilities were really the foundation of our data protection suite. And as I said, you know, enterprises around the world rely on these three capable sets of capabilities to protect their data, regardless of wherever it resides. And, um, it's really central now more than ever in the face of, you know, increasing security, um, you know, risks and compliance and the need to be able to have an always kind of available environment that customers rely on the capabilities and data protection suite to really make sure their enterprises resilient. Absolutely. >>And make sure that that data is recoverable. If anything happens, you mentioned cybersecurity. We'll get into that in a second, but so thousands of Avamar and networker customers, what are some of the key workloads and data that these customers are protecting with these technologies? >>Yeah, I mean, so actually tens of thousands, tens of thousands, tens of thousands of customers that rely on data protection suite and you know, it really, I think the, the, the strength and advantage of our portfolio is its breadth, breadth and Kip terms with client operating environments in terms of applications and databases in terms of workloads and, and, and specifically use cases. So, I mean, the breadth that we offer is unparalleled. Um, you know, pretty much when a windows, Linux, um, open VMs, NetWare, you know, kind of, you know, going back in time, a long tail of kind of operating environments and then databases, right? So everything from SQL and Oracle and Sybase and DB two to new types of databases, like, you know, the no SQL or, or content store and, and, and, um, key value store types of types of, um, no SQL, uh, schema was if you will. >>And so, and then lastly is the, the use cases, right? So being able to protect data, whether that be data that's in a data center, out in remote or branch locations or data that's out in the cloud, right. And of course, create increasingly customers are placing their data, um, in a variety of locations, on edge, on core data centers and in cloud environments. And, um, we actually have over, uh, six exabytes of capacity on our management, across public cloud environments. So, um, pretty extensive deployment of our, our data protection suite in public clouds, you know, the leading hyperscalers, um, uh, cloud environments on premises as well. >>So let's talk a little bit about the customer influence because obviously there's a very cooperative relationship that Dell has with its customers that help you achieve things. Like, for example, I saw that according to IDC, Dell technologies is number one and data protection, appliances, and software leader in the Gartner magic quadrant for data center, backup and recovery for over 20 years. Now, talk to us a little bit more about that symbiotic customer Dell. >>Yeah. So it's a great question. We see our customers and strategic partners, and we really want to understand their business, their requirements. We engage on a quarterly basis with customers and partners in, um, it advisory councils. And then of course, we are always engaging with customers outside of those cycles on a kind of a one-on-one basis. And so we're really driving the innovation and the backlogs and the roadmap for data protection suite based upon customer feedback and, um, uh, approximately 79% of the fortune 100 customers, our Dell data, Dell technologies, data protection customers. Now that's not to say that that's our only customer base. We have customers in commercial accounts in mid-market and in, uh, federal agencies. Um, but you know, we take our customer relationships really, really seriously, and we engage with them, uh, on a regular basis, both in a group forum to provide feedback as well as in a one-on-one basis. And we're building our roadmaps and our, and our, our, our product releases based on feedback from customers. And, um, again, you know, large customer base that we take very seriously, >>Right to the customer listening obviously is critical for Dell. So you talked a little bit about what that cycle looks like in terms of quarterly meetings, and then those individual meetings, what are some of the enhancements and advancements that customers have actually influenced? >>Yeah, so we, I mean, we, I think, um, continuing, continuing to provide simplicity and ease of use is a key, uh, element of our portfolio and our in our strategy, right? So continuing to modernize and update the software in terms of workflows, in terms of, uh, know common experiences, also increasingly customers want to automate their data protection process. So really taking an API first strategy for how we deliver capabilities to customers, you know, continuing to expand our client, um, database hypervisor environments, continue to extend out our cloud support. Um, you know, things like, um, protection of cloud, native applications with, uh, increasingly customers containerizing, um, and building scale-out applications. We want to be able to protect Kubernetes environment. So that's kind of an area of focus for us. Um, another area of focus for us is going deeper with our key strategic partners, you know, whether that'd be a, a cloud partner, a hypervisor partner, and then of course, customers, in fact, one of the top three things that we consistently hear from, from these councils that we do is the, the criticality of security security and or data protection environment, but the criticality of being able to be resilient from, and, and in the event of a, of a cyber attack to be able to resilient recover from that cyber attack. >>So that is an area where we continue to make, uh, innovations and investments, uh, in the data protection suite. >>And that's so critical. One of the things that we saw in the last year, 15 months, plus Laura, is this massive rise in ransomware. It's now a household word, the colonial pipeline, for example, that meat plan, it's, it's now many businesses knowing it's not if we get attacked, but it's when, so having the ability to be resilient and recover that data is table stakes for, I imagine a business in any organization, I want to understand a little bit more. So you talked about tens of thousands of customers using Avamar and networker. So now they have the capability of also expanding and using more of, of the suite. Talk to me a little bit about that. >>Yeah, so, I mean, I think it starts with the customer environment and what workloads and use cases they have and because of the breadth of capabilities and Dave, the data protection suite, you know, we really optimize the solution based upon their needs, right? So if they have, um, a large portfolio of, of applications that they need to maintain, but they're also building applications or, or, or systems for the future, we have S you know, solution there. If they have a single hypervisor strategy or a multiple hypervisor strategy, we have a, you know, a strategy there, if they have data that's on premise and across a range of public clouds, you know, one large customer we have as a, you know, kind of, uh, uh, uh, three-plus one strategy around cloud. So there's, they, there's, they're, they're leveraging, you know, three different, um, uh, public cloud, I as environments. And then they're also have their on-premise cloud environment. So, you know, we, it really starts with the customer workload and the data and where it lives, whether that's be out in an edge location in a row remote or branch office on an end point somewhere, they need to protect whether it be in a core data center or multiple data centers, or rather that be in the cloud. Um, you know, that's how we think about optimizing the solution for the, for the customers. >>Curious if you can give me any examples of customers, maybe by industry that were, have been with Dell for a long time with Avamar networker and how they've expanded, being able to pick, as you say, as their, or as their environment grows. And we've got, um, now as this blur, right, it's now work from anywhere data centers, edge. Talk to me about some customers, examples that you think really articulate the value of what Dell is doing. >>Yeah, so, I mean, I think one customer, um, in the financial services sector comes to mind. They have a large, uh, um, amount of unstructured data that they need to protect, you know, petabytes, petabytes, and petabytes of data they need to protect. And so I think that's one customer that comes to mind is someone we've been with for a long time, uh, you know, been partnering with for a long time, >>A lot of, of, um, flexibility and choice for Avamar, a networker customers, as things change the world continues to pivot. And we know it's absolutely essential to be able to recover that data. You mentioned 70, I think 79% of the fortune 100 are using, uh, Dell technologies for data protection software. That's probably something that's only going to continue to grow. Um, lots of stuff coming up, as you mentioned, but what are some of the things that you're personally excited about as the world starts to open up and you get to actually go out and engage with customers >>I'm in just looking forward to like in-person meetings, right? I mean, I just love going and trying to understand what problems the customers are trying to solve and how we can help address those. Um, I think, you know, what I see customers sort of struggling with is how do they kind of manage their current environment while they're building for the future? Um, so there's a lot of interest in questions around, you know, the, how do they protect some of these new types of workloads, whether they're deployed on premise or in the public cloud. Um, so that continues to be an area where we, you know, we continue to engage with customers. Um, I'm also really personally excited about, you know, the extensions that we're doing and our cyber recovery capabilities socio can expect to hear more about some of those in the, in the next 12 months, because we're really, um, you know, seeing that as a key, uh, driver to kind of increase, um, you know, increased policies around and, and implementations around data protection, uh, is, is because of these, you know, the, the need to be able to re be resilient from cyber attacks. >>Um, I would say we're also doing some very interesting integrations with VMware. Um, we're going to have some first and only announcements around VMware and managing protection for VMware, uh, you know, VM environments. So we can look forward to hearing more about that. And, you know, we have customers that are deployed our data protection solutions at scale. Um, you know, one customer has 150,000 clients they're protecting with our data protection offerings. Wow. 150,000. And so, you know, we're continuing to improve the, and enhance the products to meet those kinds of scale requirements. And, um, you know, I'm excited by the fact that, that we've had this long standing relationship with this one particular customer and, you know, continue to, to help and, and flow an edge where, where their needs go. >>And that's something that even a great job of talking about is just not just a longstanding relationships, but really that dedication that Dell has to innovating with its customers. Laura, thank you for sharing some of the updates of what's new, what you're continuing to do with customers and what you're looking forward to in the future. It sounds like we might hear some news around the VMworld timeframe. Yes. All right, Laura, thank you so much for joining me today. I appreciate your time. >>It's been great to be here. Thanks so much. >>Excellent for Laura Dwight and Lisa Martin, you're watching this cube conversation.

>> Narrator: From around the globe. It's theCUBE with digital coverage of Postgres vision 2021 brought to you by EDB. >> Welcome back to Postgres Vision 21. My name is Dave Vellante. We're thrilled to welcome Carl Olofsen to theCUBE. Carl is a research vice president at IDC focused on data management. The long-time database analyst is the technologist and market observer. Carl, good to see you again. >> Thanks Dave. Glad to be here. >> All right. Let's let's get into it. Let's talk about, let's go right to the, to the source the open source database space. You know, how, what changes have you seen over the last couple of years in that marketplace? >> Well, this is a dynamic area and it's continuing to evolve. When we first saw the initial open source products like mysQl and PostgreSQL on the early days they were very limited in terms of functionality. They were espoused largely by sort of true believers. You know, people who said everything should be open source. And we saw that mainly they were being used for what I would call rather prosaic database applications. But as time has gone by they both of these products improve. Now there's one key difference, of course, which is a mySQL is company owned open source. So the IP belongs to Oracle corporation. Whereas PostgreSQL is community open source, which means that the IP belongs to the PostgreSQL community. And that can have a big difference in terms of things like licensing and so forth, which really matters now that we're coming into the cloud space because as open-source products moving into the cloud space the revenue model is based on subscriptions. And of course they are always based on subscription to open source cause you don't charge for the license. So what you charge for its support, but in the cloud what you can do is you can set up a database service, excuse me, a database service and then you charge for that service. And if it's open source or it's not open source that actually doesn't matter to the user. If you see what that I mean because they still are paying a subscription fee for a service and they get the service. The main difference between the two types is that if you're a commercial provider of PostgreSQL like enterprise DB, you don't have control over where it goes and you don't have control over the IP and how people use it in different ways. Whereas Oracle owns mySQL so they have a lot more control and they can do things to it on their own. They don't have to consult the community. Now there's also, non-relational open source including MongoDB. And as you may be aware, MongoDB has changed their license. So that it's not possible for third party to offer Mongo DB as a complete managed database service without paying a license fee to MongoDB for that. And that's because they own the IP too. And we're going to see a lot more of this sort of thing. I have conversations with open source all the time and they are getting a little concerned that it has become possible for somebody to simply take their technology, make a lot of money off that. And no money goes back to the community. No money goes back to the IRS. It's a company it's just stays with the supplier. So I think, you know it'll be interesting to see how all this is over time. >> So you're suggesting that the Postgres model then is, is I guess I'll use the word cleaner. And so that feels like it's a it's a benefit or is it a two-edged sword kind of thing? I mean, you were saying before, you know a company controls the IP so they could do things without having to go to the community. So maybe they can do things faster. But at the other hand like you said, you get handcuffed. You think you're going to be able to get a, you know a managed service, but then all of a sudden you're not and the rules change midstream saying it, am I correct? That Postgres, the model is cleaner for the customer? >> Well, you know, I mean, a lot of my friends who are in the open source community don't even consider company owned open source to be true open source because the IP is controlled by a company, not by a community. >> Dave: Right >> So from that perspective certainly Postgres SQL is considered, I don't know if you want to use the word cleaner or more pure or something along those lines, but also because of that the nature of community open source it can be used in many different ways. And so we see Postgres popping up all over the place sometimes partially and sometimes altogether, in other words, a service, a cloud service, we'll take a piece of Postgres and stick it on top of their own technology and offer it. And the reason they do that is they know there are a lot of developers out there who already know how to code for Postgres. So they are immediately first-class users of the service that they're offering. >> So, talk a little bit more about what you're seeing. You just mentioned a lot of different use cases. That's interesting. I didn't realize that was, that was happening. The, what are you seeing in terms of adoption in let's say the last 18, 24 months specific to Postgres? >> Yeah, we're seeing a fair amount of adoption in especially in the middle market. And of course there is rapid adoption in the tech sector. Now, why would that be? Well it's because they have armies of technologists. Who know how to program this stuff. You know, when you, you know, a lot of them will use PostgreSQL without a contract without a support contract, they'll just support themselves. And they can do that because they have the technicians who are capable of doing it. Most regular businesses can't do that. They don't have the staff so they need that support contract. And so that's where a company like enterpriseDB comes. I mentioned them only because they're the leading supplier Postgres to all their other suppliers. >> I was talking to Josh Burgers, red hat and he was, you know, he had just come off a Cubacon and he was explaining kind of what's happening in that community. Big focus of course on security and the whole, you know, so-called shift left. We were having a good discussion about, you know when does it make sense to use, you know Postgres in a container environment should you use Postgres and Kubernetes and he sort of suggested that things have rapidly evolved. There's still, you know, considerations but what are you seeing in terms of the adoption of microservices architectures containers, generally Kubernetes how has that affected the use of things like postsgres? >> So those are all different things or need to be kind of custody. >> Pick your favorite. >> They're related then. So microservices, the microservice concept is that you take an application break it up into little pieces and each one becomes a microservice that's invoked through an API. And then you have this whole structure API system that you use to drive the application and they run. They typically, they run in containers usually Kubernetes govern containers but the reason you do this and this is basically a efficiency because especially in the cloud, you want only to pay for what you use. So when you're running a microservice based application. Applications have lots of little pieces when something needs to be done, microservice fires up it does the thing that needs to be done. It goes away. You only pay for that fraction of a second that the microservice is running. Whereas in a conventional application you load this big heavyweight application. It does stop. It sets some weights with things and does more stuff and sits and waits for things. And you pay for compute for that entire period. So it's much more cost effective to use a microservices application. The thing is that microservice, the concept of microservices is based on the idea that the code is stateless but database code isn't stateless cause it has its attraction to the database which is the ultimate kind of like stateful environment right? So it's a tricky business. Most database technologies that are claimed to be container-based actually run in containers the way they run in servers. In other words, they're not microservice-based they do run in containers. And the reason they're doing that is for portability so that you can deploy them anywhere and you can move them around. But you know deploying a microservice based database is, well, it's it's a big technical project. I mean, that is hard to do. >> Right and so talk about, I mean again we're talking to Josh it was clear that that Kubernetes has evolved, you know quite rapidly at the same time there were cautions. In other words, he would say I think suggested things like, you know, there were known at one point, there were known, you know flaws and known bugs that ship the code that's been been remediated or moderated in terms of that practice but still there's there's considerations just in terms of the frequency of updates. I think he gave the example of when was the last time you know, JVM got, you know, overhauled. And so what kind of considerations should customers think about when considering them, they want the Kubernetes they want the flexibility and the agility but at the same time, if they're going to put it production, they've got to be careful, right? >> Yeah, I think you need to make sure you're using you're using functions that are well-established, you know you wouldn't want to put something into production that's new. They say, oh, here's a new, here's a new operation. Let's try that. And then, you know, you get in trouble. So you want to deal conservative that way you know, Kubernetes is open-source so and the updates and the testing and all that follows a rather slow formal process, you know from the time that the submission comes in to the time that it goes out, whereas you mentioned JVMs JV, but it was owned by Oracle. And so JVMs are managed like products. Now there's a whole sort of legal thing I don't want to get into it as to whether it's legal. They claim it's not libero third parties to build JVMs without paying a licensing. I don't want to talk about that, but it's based on a very state that has a very stable base, you know whereas this area of Kubernetes and govern containers is still rapidly evolving but this is like any technology, right? I mean, when you, if you're going to commit your enterprise to functions that run on an emerging technology then you are accepting some risk. You know, that there's no question about it. >> So we talked about the cloud earlier and the whole trend toward managed services. I mean, how does that specifically apply to Postgres? You can kind of imagine like a sidecar, a little bit of Postgres mixed in with, you know, other services. So what do you see and what do you, what's your telescope say in terms of the the Postgres adoption cloud? How do you see that progressing? >> I think there's a lot of potential. There's a lot of potential there. I think we are nowhere near the option that it should be able to achieve. I say that because for one thing, even though we analyze the future at IDC, that doesn't mean we actually know the future. So I can't say what its adoption will be but I can say that there's a lot of potential there. There's a tremendous number of Postgres developers out there. So there's a huge potential for adoption. And especially in cloud adoption, the main thing that would help that is independent. And I know that enterpriseDB has one independent a managed cloud service. So I think they do. >> Yeah I think so. >> But you know, why do I say that? I say that because alternatives these days there are some small companies that maybe they'll survive and maybe they won't, but that, you know, do you want to get involved with them or the cloud platform providers, but if you use their Postgres you're locked into that cloud platform. You know, if you use Amazon, go press on RDS, right? You're not, you become quickly locked in because you're starting using all the AWS tools that surround it to build and manage your application. And then you can't move. If you see what I mean. >> Dave: Yeah . >> They have have an RDS labor Aurora, and this is actually one of the things that it's really just a thin layer of Postgres interaction code underneath Aurora is their own product. so that's an even deeper level of commitment. >> So what has to happen for, so obviously cloud, you know, big trend. So the Postgres community then adopts the code base for the cloud. Obviously EDB has, you know hundreds of developers contributing to that, but so what does that mean to be able to run in the cloud? Is that making it cloud native? Is that extensions? Is it, you know, what technically has to occur and what has occurred and how mature is it? >> Well, so smaller user organizations are able to migrate fairly quickly cloud because most of their applications are you know, commercially purchased. They're like factories applications. When they move to the cloud, they get the SAS one and often the SAS equivalent runs on Postgres. So that's just fine. Larger enterprises are a real mess. If you've ever been in a large enterprise data center you know what I'm talking about? It's just, there's just servers and storage everywhere. There's, all these applications, databases connections. They are not moving to the cloud anytime soon. But what they are doing is setting up things like private cloud environments and applying in there. And this is a place where if you're thinking about moving to something like a Postgres you know most of these enterprises use the big commercial databases. Oracle SQLserver DB two and so forth. If you're thinking of moving from that to a a PostgreSQL development say, then the smart thing to do would be first to do all your work in the private cloud where you'd have complete control over the environment. It also makes sense still to have a commercial support contract from a vendor that you trust, because I've said this again, unless you are, you know, Cisco or somebody, you know, some super tech company that's got all the technicians you need to do the work. You really don't want to take on that level of risk. If you see that, I mean. Another advantage to working with a supplier, a support supplier, especially if you have a close, intimate relationship is they will speed your security patches on a regular basis which is really important these days, because data security is as you know, a growing concern all over the place. >> So let's stay on the skillsets for a minute. Where do you see the gaps within enterprises? What kind of expertise you mentioned, you know support contracts, what are the types of things that a customer should look for in terms of the the expertise to apply to supporting Postgres databases? >> Well, obviously you want them to do the basics that any software company does, right? You want them to provide you with regular updates and binary form that you can load and, you know test and run. You want to have the you know, 24 hour hotline you know, telephone support, all that kind of thing. I think it's also important to have a solid ability on the part of the vendor that you're working with to provide you with advice and counseling as you, especially, if you're migrating from another technology, help your people convert from what they were using to what they're going to be using. So those are all aspects that I would look for in a vendor for supporting a product like PostgreSQL. >> When you think about the migration to the cloud, you know of course Amazon talks a lot about cloud migration. They have a lot of tooling associated with that. >> Carl: Right. >> But when you step back and look at it it did to a point earlier, I mean a lot of the hardcore mission, critical stuff isn't going to move it, hasn't moved, but a lot of the fat middle, you know, is, are good candidates for it. >> Carl: Right. >> How do you think about that? And how do you look at that? I mean, obviously Oracle is trying to shove everything into OCI and they're, you know, they're all in because they realized that could make a lot of money doing that. But what do you, what are the sort of parameters that we should think about when considering that kind of migration, moving a legacy database into the cloud? >> Well, it has to be done piecemeal. You're not going to be able to do it all at once. You know, if you have hundreds of applications, you're not just you don't even want to, you know, it's a good time to take you into it. And what you've got running, ask yourself are these applications really serving the business interests today and will they in the future or is this a good time to maybe consider something else? Even if you have a packaged application, there might be one that is more aligned with your future goals. So it's important to do that. Look at your data integration, try to simplify it. You know, most data integration that most companies has done piecemeal project by project. They don't reference each other. So you have this chaos of ETL jobs and transformation rules and things like that that are just, you know, even difficult to manage. Now, just forget about any kind of migration or transformation considerations, just trying to run it now is becoming increasingly difficult. You know, maybe you want to change your strategy for doing data integration. Maybe you want to consolidate you want to put more data in one database. I'm not an advocate of the idea that you can put all application data in one database by the way, we know from bitter experience that doesn't work, but we can be rational about the kinds of databases that we use and how they sit together. >> Well, I mean, you've been following this for a long time and you saw the sort of rise and fall of the big data meme. And you know, this idea that you can shove everything into a single place, have a single version of the truth. It's like, it's just never seemed to happen. >> Carl: Right. >> So, you know, Postgres has been around a long time. It's evolved. I mean, I remember when, you know, VMware's ascendancy and people are like, okay, should I, you know should I virtualize my Postgres database is your, you know similar conversations that we were having earlier about Kubernetes. You've seen the move to the cloud. We're going to have this conversation about the edge at some point in time. So what's your outlook for Postgres, the Postgres community and, you know database market overall? >> Well, I really think the future for database growth is in the cloud. That's what all the data we're looking at and the case that's what our recent surveys indicate. As I said before, the rate of change depends on the size of the enterprise. Smaller advices are moving rapidly, large enterprises much more slowly and cautiously for the very simple reason that it's a very complex proposition. And also in some cases, they're wondering if they can move certain data or will they be violating your some sort of regulatory constraint or contractual issue. So they need to deal with those things too. That's why the private cloud is the perfect place to get started and get technology all lined up storing your data center is still under your control no legal issues there, but you can start, you know converting your applications to micro-service architected applications running in containers. You can start replacing your database servers with ones that can run in a container environment and maybe in the future, maybe hope that in the future, some of those will actually also be able to run as microservices. I don't think it's impossible but it just involves programming the database server in a very different way than we've done in the past. But you do those things. You can do those things under your own control over time in your own dataset. And then you reach a point where you want to take the elements of your application environment and say, what pieces of this, can I move to the cloud without creating disruption and issues regarding things like data egress and latency from cloud to data center and that kind of thing. And prepare for that. And then you're doing the step wise and then you start converting in a stepwise manner. I think ultimately it just makes so much sense to be in the cloud that the cloud vendors have economies of scale. They can deploy large numbers of servers and storage systems to satisfy the needs of large numbers of customers and create, you know great considerable savings. Some of which of course becomes their profit which is what's due to them. And some of that comes back to the users. So that's what I expect. We're going to see. And oh gosh, I would say that starting from about three years from now the larger enterprises start making their move and then you'll really start to see changes in the numbers in terms of cloud and cloud revenue. >> Great stuff, Carl, thank you for that. So any cool research you're working on lately, how you're spending your your work time, anything you want to plug? >> Well, working a lot on just as these questions, you know cloud migration is a hot topic, another which is really sort of off the subject. And what we've been talking about is graph database which I've been doing a fair amount of research into. I think that's going to be really important in the coming years and really, you know working with my colleagues in a project called the future of intelligence which looks at all the different related elements not just database, data integration but artificial intelligence, data communications and so on and so forth and how they come together to create a more intelligent enterprise. And that's a major initiative that I see. It's one of the, we call the future of initiatives. >> Great, Carls, thanks so much for coming back to theCUBE. It's great to have you, man. I appreciate it. >> Well, I enjoyed it. Now I have to do it again sometime. >> All right you got it. All right thank you everybody for watching theCUBEs. Continuous coverage of Postgres vision 21. This is Dave Vellante keep it right there. (upbeat music)

>> From around the globe, it's the cube, with digital coverage of IBM think 2021 brought to you by IBM. >> Hi everybody, Welcome back to IBM Think 2021. My name is Dave Vellante and you're watching the cubes continuous coverage of this event. We go out to the events, we extract the signal from the noise but doing that virtually for the better part of a 14 months now, we're going to get deeper into application modernization. Marie Ashway is here. She's the director of marketing at Mainline Information Systems and David burrows. Burrows is an account executive at Mainline folks. Welcome to the cube, great to have you on today. >> Thank you. Nice to be here. >> Marie, I want to start with, with Mainline. A lot of people might not be familiar with Mainline but you've transformed over the past five years. I wonder if you could describe that for our audience? >> Yes, we have, indeed we have, Mainline, you know is a 30 plus year company and, and for 30 odd years we had really been focused a lot in hardware, right? Hardware reselling. That's what the market needed. That's what we did a lot of. But then in the past I would say five to eight years, maybe even 10 years we started on this transformation project for the business where we started transforming ourselves into really systems integrators versus just hardware reseller. So now we can go to a client and we can say, Hey now what are you struggling with? Right? What are your business challenges? And then from there we can integrate a solution that might be hardware. It might be software, it might be some services it could be managed services. It could be staffing services could be a number of different things and put all that together and then deliver a complete solution that helps them with their, their business requirements. >> Well, David, that that must've been an interesting transition because what Marie just described as it used to be every opportunity was a nail and whatever box you were selling was the hammer. And that, that has changed dramatically. Of course. So you, you, I wonder what that discussion was like with, with, with clients. You must have heard that early on and said, Uh-Oh this cloud thing is happening. The world is changing. We've got to change too. I wonder if you could chime in on that transformation. >> Yes. That's our, as our clients have been changing what we've been doing is, you know, making sure that we fully understand what's available, not only in the marketplace, but the competition. What, what each industry segment, for example, banking versus insurance versus a utility maybe facing during this this time. And so, you know, being able to transform as a as an accounting dedicated, we've been able to indicate. And so provide solutions as Marie indicated, the large focus over the last five years has been networking and security. As we move more compute to the edge, close to the edge security has been predominant. And so, you know, hardware is really almost commoditized through and through and with the exception of, you know IBM, Z and, and power. And so, you know, we've had to really sellers, you know focus on what customers are dealing with and how they transition. And as we, you know, through COVID, it's actually been a bigger challenge, a bigger focus on security. And I think we'll talk about that a little bit later in more detail >> Let's, let's, let's do that now. So, so Marie, maybe at a high level, you could talk about those challenges that your clients are facing. And then we can sort of double click on how that was exacerbated by, by COVID. And I'm really interested in your perspectives on sort of the post isolation economy and how those challenges are going to shift, but, but maybe maybe kick us off at the high level if you could. >> Sure. So, so, you know, people, companies were moving toward the the whole digital transformation, right? Probably for the past three to four years we started seeing more and more that constantly everybody sees those buzzwords all the time. So clients were shifting in that direction and we were shifting to try to satisfy them with their needs with those solutions but then came COVID and all of a sudden, right. What people were, were planning on doing for the next, let's say five years. I mean, most of the CEO's were saying, yeah we're going to get there in five years. Well, that had to happen. Right. It had to have brakes went on and it had to happen instantaneously. So that put a big change in focus a big change in direction for not only our clients. Right. But for our own folks, folks like David who are trying to service these clients with having to bring them these solutions that we're going to solve their digital business needs today and not five years from now. >> Yeah. So David let's, let's talk about that. I mean, what Marie just described I call it the forced march to digital, because as Marie as you were saying, people were on a digital transformation but there was a little bit of complacency and okay, we'll get there. We're really busy doing some other stuff. And then all of a sudden you've probably seen the meme of the COVID wrecking ball coming, coming into the building the office building and saying, you know, you know, well we're doing fine and all of a sudden, boom the forced COVID comes in. So, so, so so how did that affect your clients and how did you respond? I mean, they're asking for VDI and get me some laptops, I need end point security. And so how did that affect the the application modernization efforts and David maybe you could comment on that. >> So I, I think for, for me, the biggest challenge was all business, the competition within business to survive COVID, you know, they had to put on first thing was how do we get our, our customer supported correctly? And how do we get our workers supported working at home? So the very first thing we did over the initial six months was most companies had to transform immediately within the first 30 to 90 days to allow their workforce to work from home. That happened throughout my, my customer base, both in Southern California, was customers really focused on how do we do business process? How do we compete in this marketplace and get return on investment speed, you know, time to value or what we invest in these COVID times so that we can compete with other businesses that are trying to stay alive through this transition. And, and now, you know, we're seeing on the, on the back end you know, that time, the value in terms of investment is even more important because some businesses have been significantly impacted from knowing cashflow, but you know, certainly in terms of profitability during this time. >> Makes sense. And so, Marie, so we were talking earlier about the sort of the initial path to digital transformation. And I wonder if that's got to be course corrected. I would think we were forced in to compress, you know the digital reality and, and I guess in a way that's good but in a way it was, we probably made a lot of mistakes. It was a bit of a Petri dish. So now as we begin to ,knock on, exit COVID you would think those those imperatives adjust and they start to become aligned. What's your take on that, especially as it relates to application and infrastructure modernization. >> So I would agree with that. I think that there's definitely has to be a little bit of a of a realignment happening. And I know recently I read that 2021 is expected to be a very large year in IT spend because all of those initiatives that CEOs and others were going after pre COVID kind of got put on hold, right. So they could then go focus on all of those digital means that were needed. Like, you know, the CDI, you know, work at home all the security stuff for that. So I think we're going to see, I'm thinking, we're going to see a shift again now, and maybe businesses are going to go back and try to pick up where they were prior to COVID and now start working on more of really of the application modernization initiatives that were in mind. And I know we wanted to talk about that as well because David's been working on quite a bit of application modernization with a few of his clients as we're seeing again, businesses change. And, and I don't know that all of that changes because of COVID. I think all of that change was for their competitiveness, to get there anyway. So I think that's going to start, as you said before, David I think it's going to start now having to kind of rethink up >> It reminds me of traffic on the M four David have you ever been to driving in in London when it's slingshots? Right? It's that's what's COVID was like Marie, you're absolutely right. Last year IT spend was down 4 to 5% this year. I mean, our prediction is it's going to be in the 6 to 7% range, which, which, which kind of aligns with where Gartner and IDC are based on our surveys. But, you know, back in, in April like I think the 16th of April it was a headline of wall street journal that the China grew 18% GDP in the quarter. So it's very hard to predict, but, but it's coming back you know, we can see that David and so so spending is really going to accelerate. There's probably some pent-up demand for that application modernization. Maybe it's been a little bit neglected as we've done as Maria was saying. And you were saying that work from home. So maybe you could talk a little bit more about the modernization aspects and maybe I'm really interested in the things that you guys deliver in your portfolio with IBM. >> Sure. So what I have customers in multiple phases within this current digital transformation there are customers moving everything to the next gen development which is a fully containerized code. Being able to, you know swiftly go through their development tests and hybrid cloud environments where they're they haven't made an investment yet but they're sampling what it might be like to change into that world. And then there are customers are still in the typical environment, the traditional environment and are looking at what the solutions as far as packages are available for them moving forward. So they can kind of skip over any kind of development and being able to leverage what I call next gen development or next gen systems immediately as you know, you ask, you know, what are the what are the systems that are available? IBM's cloud pack solution set. It provides a portfolio of capabilities both in the application suite, database suite, security. I have customers today leveraging that. And, and so that is one of the first pieces that that customers I see who are on the leading edge or are also kind of trailing, are looking at these cloud bags to be able to go time to market and time to value quickly. >> Yeah. So when I look at your portfolio I just sort of scan the web. David just mentioned Marie, cloud pack. I mean, we're talking software here. You guys do have a lot of expertise in Z, Z Linux, Power. You mentioned is not a commodity. And it's one of the few pieces of hardware that and Z they're not a commodity. Storage, I would think business resiliency fits in there beyond disaster recovery. You Red Hat, we're talking, you know, things like open open shift and Ansible for automation. So these are, these are not your grandfather's main line. These are toolkits are a piece of, you know, parts of the tool bag that you bring to bear to focus on on client outcomes and solutions is am I getting that right? >> Yes, absolutely. Absolutely. And again, right, that goes back to the original opening comment about how we've transformed as a business, right. To become an integrator putting all of these different pieces together. I mean, I know that something that that David recently had worked on, Oh my goodness. If you would have looked at the list of pieces of elements to that solution, it was really quite incredible between open source stuff, you know and a bunch of IBM stuff. Yes. It was some storage and yeah, there was some power. Yes. There was Red Hat. Right. But then there was other stuff, there was VMware there was some things that I can't even remember now all the names to all the components, but it was it was a laundry list. Right. And so that's where though Mainline stepped in and put the pieces together for the customer so that the customer then can get done what they needed to get done, which was which was really solve their business problem which was trying to become more competitive in their market space. >> Okay, David, so when Marie was just staying was basically my takeaway is, is this a system integrator? You've got all these piece parts with these technologies you've got virtualization, you've got automation you've got containers and so forth. And yes, there's there's hardware but there's this integration that has to occur. And your job is to abstract that complexity that underlying complexity away so that the customers can focus on the outcome. Maybe you could talk about that and how you do that. >> Sure. I'll, I'll give you a good example of a recent customer that we work with, who was basically implying, we consider an enterprise data platform that, that was going to rework their entire data warehouse into something that had governance surrounding it where they could validate all the data that was coming into their warehouse. And so we underpinned that with an infrastructure of power. We're running, obviously IBM pack for data with DB two warehouse. We use a combination of that with five-year data flow through IBM with the streaming and the governance IBM governance catalog piece, which is lots of knowledge catalog. So we've been able to take not only what their base requirements were, but all the microservices that are packaged in with cloud pack, all running on open shift which was a great acquisition that IBM did last year. And then they also required other microservices outside to support that environment >> Paint a picture for us as to what the future looks like. It's, it's much different than the past 30 years and bring us home, please. >> Sure. So I think the future for us is to continue to to find all of the solutions that will that will help our customers, you know get to their next steps. Right. And, and there's a lot, as you know did this lots of solutions out there. There's lots of new companies that are popping up all the time. You know, inherently, you know, Mainline is an IBM partner. We've been an IBM partner for 30 plus years since our inception. And that's the base of our business is, is IBM. But, but there are other requirements that are needed by by businesses, by our customers. And that's where we, we reach out and partner up. We probably have gone my goodness 200 plus partnerships with various companies various technology companies that we can then lean on and pull in those ancillary solutions to, to to complete that, that solution for the customer. So I think we're going to continue going down that path. We're going to continue making sure that we're partnered with the, the, the leading technology companies. So we can build that IBM solution for our customer and and bolt on the other pieces that are needed. We're going to continue to grow and enhance our services business because we've got quite a large services business whether it's implementation services, we do manage services we have staffing services. I think you're going to see we're still going to continue to, to grow that business because that is a piece where companies, you know they don't want to worry about running all of that stuff, right. They want to know that their system's going to be running 24 seven. And if there is a bump or a burp or something happens, Hey they could pick up the phone, they can call Mainline. We can help them get things corrected. So I think we're going to still see a lot of that going on as well within our, our, our offerings. >> Excellent. Well, congratulations for making it through that. Not a whole lot, not not every hardware seller reseller made it through and you guys transformed. It's a, it's an inspiring story. Marie, David, thanks so much for coming on the cube. >> Thank you. >> Thank you very much. >> You're really welcome. And thank you for watching everybody. This is Dave Volante in our continuous coverage and the cube of IBM think 2021. Keep it right there.

>>from around the globe. It's the Cube with coverage of Coop Con and Cloud, Native Con Europe 2020 Virtual brought to You by Red Hat, The Cloud Native Computing Foundation and its Ecosystem Partners. >>And welcome back to the Cube's coverage of Cube Con Cloud, Native Con Europe 20 twenties Virtual event. I'm Stew Minimum and and happy to Welcome back to the program, two of our Cube alumni. We're gonna be talking about storage in this kubernetes and container world. First of all, we have Sam Warner. He is the vice president of storage, offering management at IBM, and joining him is Brent Compton, senior director of storage and data architecture at Red Hat and Brent. Thank you for joining us, and we get to really dig in. It's the combined IBM and red hat activity in this space, of course, both companies very active in the space of the acquisition, and so we're excited to hear about what's going going. Ford. Sam. Maybe if we could start with you as the tee up, you know, Both Red Hat and IBM have had their conferences this year. We've heard quite a bit about how you know, Red Hat the solutions they've offered. The open source activity is really a foundational layer for much of what IBM is doing when it comes to storage, you know, What does that mean today? >>First of all, I'm really excited to be virtually at Cube Con this year, and I'm also really excited to be with my colleague Brent from Red Hat. This is, I think, the first time that IBM storage and Red Hat Storage have been able to get together and really articulate what we're doing to help our customers in the context of kubernetes and and also with open shift, the things we're doing there. So I think you'll find, ah, you know, as we talked today, that there's a lot of work we're doing to bring together the core capabilities of IBM storage that been helping enterprises with there core applications for years alongside, Ah, the incredible open source capabilities being developed, you know, by red Hat and how we can bring those together to help customers, uh, continue moving forward with their initiatives around kubernetes and rebuilding their applications to be develop once, deploy anywhere, which runs into quite a few challenges for storage. So, Brennan, I'm excited to talk about all the great things we're doing. Excited about getting to share it with everybody else. A cube con? >>Yes. So of course, containers When they first came out well, for stateless environments and we knew that, you know, we've seen this before. You know, those of us that live through that wave of virtualization, you kind of have a first generation solution. You know what application, What environment and be used. But if you know, as we've seen the huge explosion of containers and kubernetes, there's gonna be a maturation of the stack. Storage is a critical component of that. So maybe upfront if you could bring us up to speed you're steeped in, you know, a long history in this space. You know, the challenges that you're hearing from customers. Uhm And where are we today in 2020 for this? >>Thanks to do the most basic caps out there, I think are just traditional. I'm databases. APS that have databases like a post press, a longstanding APS out there that have databases like DB two so traditional APs that are moving towards a more agile environment. That's where we've seen in fact, our collaboration with IBM and particularly the DB two team. And that's where we've seen is they've gone to a micro services container based architecture we've seen pull from the market place. Say, you know, in addition to inventing new Cloud native APS, we want our tried true and tested perhaps I mean such as DB two, such as MQ. We want those to have the benefits of a red hat, open shift, agile environment. And that's where the collaboration between our group and Sam's group comes in together is providing the storage and data services for those state labs. >>Great, Sam, you know I IBM. You've been working with the storage administrator for a long time. What challenges are they facing when we go to the new architectures is it's still the same people it might There be a different part of the organization where you need to start in delivering these solutions. >>It's a really, really good question, and it's interesting cause I do spend a lot of time with storage administrators and the people who are operating the I T infrastructure. And what you'll find is that the decision maker isn't the i t operations or storage operations. People These decisions about implementing kubernetes and moving applications to these new environments are actually being driven by the business lines, which is, I guess, not so different from any other major technology shift. And the storage administrators now are struggling to keep up. So the business lines would like to accelerate development. They want to move to a developed, once deploy anywhere model, and so they start moving down the path of kubernetes. In order to do that, they start, you know, leveraging middleware components that are containerized and easy to deploy. And then they're turning to the I T infrastructure teams and asking them to be able to support it. And when you talk to the storage administrators, they're trying to figure out how to do some of the basic things that are absolutely core to what they do, which is protecting the data in the event of a disaster or some kind of a cyber attack, being able to recover the data, being able to keep the data safe, ensuring governance and privacy of the data. These things are difficult in any environment, but now you're moving to a completely new world and the storage administrators have ah tough challenge out of them. And I think that's where IBM and Red Hat can really come together with all of our experience and are very broad portfolio with incredibly enterprise hardened storage capabilities to help them move from their more traditional infrastructure to a kubernetes environment. >>Maybe if you could bring us up to date when we look back, it, like open stack of red hat, had a few projects from an open source standpoint to help bolster the open source or storage world in the container world. We saw some of those get boarded over. There's new projects. There's been a little bit of argument as to the various different ways to do storage. And of course, we know storage has never been a single solution. There's lots of different ways to do things, but, you know, where are we with the options out there? What's that? What's what's the recommendation from Red Hat and IBM as to how we should look at that? >>I wanna Bridget question to Sam's earlier comments about the challenges facing the storage admin. So if we start with the word agility, I mean, what is agility mean for it in the data world. We're conscious for agility from an application development standpoint. But if you use the term, of course, we've been used to the term Dev ops. But if we use the term data ops, what does that mean? What does that mean to you in the past? For decades, when a developer or someone deploying production wanted to create new storage or data, resource is typically typically filed a ticket and waited. So in the agile world of open shift in kubernetes, it's everything is self service and on demand or what? What kind of constraints and demands that place on the storage and data infrastructure. So now I'll come back to your questions. Do so yes. At the time, that red hat was, um, very heavily into open stack, Red Hat acquired SEF well acquired think tank and and a majority of the SEF developers who are most active in the community. And now so and that became the de facto software defying storage for open stack. But actually for the last time that we spoke at Coop Con and the Rook project has become very popular there in the CN CF as away effectively to make software defined storage systems like SEF. Simple so effectively. The power of SEF, made simple by rook inside of the open shift operator frame where people want that power that SEF brings. But they want the simplicity of self service on demand. And that's kind of the diffusion. The coming together of traditional software defined storage with agility in a kubernetes world. So rook SEF, open shift container storage. >>Wonderful. And I wonder if we could take that a little bit further. A lot of the discussion these days and I hear it every time I talk to IBM and Red Hat is customers air using hybrid clouds. So obviously that has to have an impact on storage. You know, moving data is not easy. There's a little bit of nuance there. So, you know, how do we go from what you were just talking about into a hybrid environ? >>I guess I'll take that one to start and Brent, please feel free to chime in on it. So, um, first of all, from an IBM perspective, you really have to start at a little bit higher level and at the middleware layer. So IBM is bringing together all of our capabilities everything from analytics and AI. So application, development and, uh, in all of our middleware on and packaging them up in something that we call cloud packs, which are pre built. Catalogs have containerized capabilities that can be easily deployed. Ah, in any open shift environment, which allows customers to build applications that could be deployed both on premises and then within public cloud. So in a hybrid multi cloud environment, of course, when you build that sort of environment, you need a storage and data layer, which allows you to move those applications around freely. And that's where the IBM storage suite for cloud packs was. And we've actually taken the core capabilities of the IBM storage software to find storage portfolio. Um, which give you everything you need for high performance block storage, scale out, um, file storage and object storage. And then we've combined that with the capabilities, uh, that we were just discussing from Red Hat, which including a CS on SEF, which allow you, ah, customer to create a common, agile and automated storage environment both on premises and the cloud giving consistent deployment and the ability to orchestrate the data to where it's needed >>I'll just add on to that. I mean that, as Sam noted and is probably most of you are aware. Hybrid Cloud is at the heart of the IBM acquisition of Red Hat with red hat open shift. The stated intent of red hat open shift is to be to become the default operating environment for the hybrid cloud, so effectively bring your own cloud wherever you run. So that that is at the very heart of the synergy between our companies and made manifest by the very large portfolios of software, which would be at which have been, um, moved to many of which to run in containers and embodied inside of IBM cloud packs. So IBM cloud packs backed by red hat open shift on wherever you're running on premises and in a public cloud. And no, with this storage suite for cloud packs that Sam referred to also having a deterministic experience. That's one of the things as we work, for instance, deeply with the IBM DB two team. One of the things that was critical for them, as they couldn't have they couldn't have their customers when they run on AWS have a completely different experience than when they ran on premises, say, on VM, where our on premises on bare metal critical to the DB two team t give their customers deterministic behavior wherever they can. >>Right? So, Sam, I I think any of our audience that it followed this space have heard Red House story about open shift in how it lives across multiple cloud environments. I'm not sure that everybody is familiar with how much of IBM storage solutions today are really this software driven. So ah, And therefore, you know, if I think about IBM, it's like, okay, and by storage or yes, it can live in the IBM Cloud. But from what I'm hearing from Brent in you and from what I know from previous discussion, this is independent and can live in multiple clouds, leveraging this underlying technology and can leverage the capabilities from those public cloud offers. That right, Sam? >>Yeah, that's right. And you know, we have the most comprehensive portfolio of software defined storage in the industry. Maybe to some, it's ah, it's a well kept secret, but those that use it No, the breadth of the portfolio. We have everything from the highest performing scale out file System Teoh Object store that can scale into the exabytes. We have our block storage as well, which runs within the public clouds and can extend back to your private cloud environment. When we talk to customers about deploying storage for hybrid multi cloud in a container environment, we give them a lot of houses to get there. We give them the ability to leverage their existing san infrastructure through the CS I drivers container storage interface. So our whole, uh, you know, physical on Prem infrastructure supports CS I today and then all the software that runs on our arrays also supports running on top of the public clouds, giving customers then the ability to extend that existing san infrastructure into a cloud environment. And now, with storage suite for cloud packs a sprint described earlier, we give you the ability to build a really agile infrastructure, leveraging the capabilities from Red Hat to give you a fully extensible environment and a common way of managing and deploying both on Prem and in the cloud. So we give you a journey with our portfolio to get from your existing infrastructure. Today, you don't have to throw it out it started with that and build out an environment that goes both on Prem and in the cloud. >>Yeah, Brent, I'm glad that you started with database, cause it's not something that I think most people would think about. You know, in a kubernetes environment, you Do you have any customer examples you might be able to give? Maybe Anonymous? Of course. Just talking about how those mission critical applications can fit into the new modern architect. The >>big banks. I mean, just full stop the big banks. But what I'd add to that So that's kind of frequently they start because applications based on structured data remain at the heart of a lot of enterprises. But I would say workload, category number two, our is all things machine Learning Analytics ai and we're seeing an explosion of adoption within the open shift. And, of course, cloud pack. IBM Cloud private for data, is a key market participant in that machine learning analytic space. So an explosion of the usage of of open shift for those types of workloads I was gonna touch just briefly on an example, going back to our kind of data data pipeline and how it started with databases, but it just it explodes. For instance, data pipeline automation, where you have data coming into your APS that are kubernetes based that our open shift based well, maybe we'll end up inside of Watson Studio inside of IBM ah, cloud pack for data. But along the way, there are a variety of transformations that need to occur. Let's say that you're a big bank. You need Teoh effectively as it comes in. You need to be able to run a CRC to ensure to a test that when when you modify the data, for instance, in a real time processing pipeline that when you pass it on to the next stage that you can guarantee well that you can attest that there's been no tampering of the data. So that's an illustration where it began, very with the basics of basic applications running with structured data with databases. Where we're seeing the state of the industry today is tremendous use of these kubernetes and open shift based architectures for machine learning. Analytics made more simple by data pay data pipeline automation through things like open shift container storage through things like open shift server lis or you have scale double functions and what not? So yeah, it began there. But boy, I tell you what. It's exploded since then. >>Yeah, great to hear not only traditional applications, but as you said so, so much interest. And the need for those new analytics use cases s so it's absolutely that's where it's going. Someone. One other piece of the storage story, of course, is not just that we have state full usage, but talk about data protection, if you could, on how you know things that I think of traditionally my backup restore and like, how does that fit into the whole discussion we've been having? >>You know, when you talk to customers, it's one of the biggest challenges they have honestly. And moving to containers is how do I get the same level of data protection that I use today? Ah, the environments are in many cases, more complex from a data and storage perspective. You want Teoh be able to take application consistent copies of your data that could be recovered quickly, Uh, and in some cases even reused. You can reuse the copies, for they have task for application migration. There's there's lots of or for actually AI or analytics. There's lots of use cases for the data, but a lot of the tools and AP eyes are still still very new in this space. IBM has made, uh, prior, uh, doing data protection for containers. Ah, top priority for our spectrum protect suite. And we provide the capabilities to do application aware snapshots of your storage environment so that a kubernetes developer can actually build in the resiliency they need. As they build applications in a storage administrator can get a pane of glass Ah, and visibility into all of the data and ensure that it's all being protected appropriately and provide things like S L A. So I think it's about, you know, the fact that the early days of communities tended to be stateless. Now that people are moving some of the more mission critical workloads, the data protection becomes just just critical as anything else you do in the environment. So the tools have to catch up. So that's a top priority of ours. And we provide a lot of those capabilities today and you'll see if you watch what we do with our spectrum. Protect suite will continue to provide the capabilities that our customers need to move their mission. Critical applications to a kubernetes environment. >>Alright And Brent? One other question. Looking forward a little bit. We've been talking for the last couple of years about how server lists can plug into this. Ah, higher kubernetes ecosystem. The K Native project is one that I, IBM and Red Hat has been involved with. So for open shift and server lis with I'm sure you're leveraging k native. What is the update? That >>the update is effectively adoption inside of a lot of cases like the big banks, but also other in the talk, uh, the largest companies in other industries as well. So if you take the words event driven architecture, many of them are coming to us with that's kind of top of mind of them is the need to say, you know, I need to ensure that when data first hits my environment, I can't wait. I can't wait for a scheduled batch job to come along and process that data and maybe run an inference. I mean, the classic cases you're ingesting a chest X ray, and you need to immediately run that against an inference model to determine if the patient has pneumonia or code 19 and then kick off another serverless function to anonymous data. Just send back in to retrain your model. So the need. And so you mentioned serverless. And of course, people say, Well, I could I could handle that just by really smart batch jobs, but kind of one of the other parts of server less that sometimes people forget but smart companies are aware of is that server lists is inherently scalable, so zero to end scalability. So as data is coming in, hitting your Kafka bus, hitting your object store, hitting your database and that if you picked up the the community project to be easy, Um, where something hits your relational database and I can automatically trigger an event onto the Kafka bus so that your entire our architecture becomes event >>driven. All right. Well, Sam, let me give you the funding. Let me let you have the final word. Excuse me on the IBM in this space and what you want them to have his takeaways from Cube con 2020 Europe. >>I'm actually gonna talk to I think, the storage administrators, if that's OK, because if you're not involved right now in the kubernetes projects that are happening within your enterprise, uh, they are happening and there will be new challenges. You've got a lot of investments you've made in your existing storage infrastructure. We had IBM and Red Hat can help you take advantage of the value of your existing infrastructure. Uh, the capabilities, the resiliency, the security of built into it with the years. And we can help you move forward into a hybrid, multi cloud environment built on containers. We've got the experience and the capabilities between Red Hat and IBM to help you be successful because it's still a lot of challenges there. But But our experience can help you implement that with the greatest success. Appreciate it. >>Alright, Sam and Brent, Thank you so much for joining. It's been excellent to be able to watch the maturation in this space of the last couple of years. >>Thank you. >>Alright, we'll be back with lots more coverage from Cube Con Cloud, native con Europe 2020 the virtual event. I'm stew Minimum And thank you for watching the Cube. Yeah, yeah, yeah, yeah

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. You're watching the cube, the leader in live tech coverage, and we're covering the IBM data and AI forum in the port of Miami. Mateus Fuka is here, he's the director of offering management for hybrid data management. Everything data. But see, it's great to see you. It's great to have you. So be here with you. We're going to talk database, we're gonna talk data warehouse, everything. Data, you know, did the database market, you know, 10 years ago, 12 years, it was kind of boring. Right. And now it's like data's everywhere. Database is exploding. What's your point of view on what's going on in the marketplace? You know, I mean it's funny too. You used to it boring because I think it's the boring stuff that really matters nowadays to get, get things to where you get people to value with the solutions you want to be or the modernization. >>Thea. Yeah. Seeking to do on the data estates. Um, the challenge that you have in embracing multi-cloud data architectures. So to get, to get to, well you have to, do, I have to take care of the boring stuff. How real is multi-cloud? I mean, I know multi-cloud is, is real and that everybody has multiple clouds. But is multi-cloud a strategy or is it a sort of a symptom of multi-vendor and it just, we could have ended up here with the shadow it and everything else. >> I think it's a reality and yes, it should be a strategy, but I think more more clients and not they find themselves being exposed to this as a reality with different lines of businesses, acquiring data, um, estates running on different locations, different clouds, you know, and then companies have challenge if you want to bring it all together and actually the value of that data, um, and make it available for analytics or AI solutions. >>You know, you've got to have a strategy. >> So IBM is one of the few companies that has both a cloud and an aggressive multi-cloud strategy. Um, you know, Amazon's got outpost a little bit here and Microsoft I guess has some stuff, uh, a but, but generally speaking, uh, Oracle has got a little bit here but IBM has both a cloud. So you'd love people to come into your cloud, but you recognize not everybody's gonna come in your club. So you have an aggressive multi-cloud strategy. Why is that? What's the underpinning of that strategy? Is it openness? Is it just market, you know, total available market? Why? So first of all, yes, we have a, we have a strong portfolio on IBM cloud and we think, you know, it's the best in terms of, you know, integration with other cloud services, the performance you get on the different data services. >>But we also have a strategy that says we want to be our clients want to go. And many clients might have committed already on a strategic level to a different cloud, whether that's AWS, you know, why IBM cloud or Asia. And so we want to be ready as clients want to go. And our commitment is to offer them a complete portfolio of data services that support different workloads. And a complete portfolio in terms of, um, your, the IBM, uh, hope heavy set of technologies as well as open source technologies, give clients choice but then make them available across that universe of multicloud hybrid cloud on premise in a way that they get a consistent experience. And you know, I mean you are familiar with the term. Oh, you divide and conquer, right? I like to talk about it as uh, you know, um, unify to conquer. >>So our, our mission is really unified experience and unified the access to different capabilities available across multicloud architects. So is that really the brand promise gotta unify across clouds? Absolutely. That's our mission. And what's the experience like today and what is sort of the optimal outcome that you guys are looking for? Uh, being able to run any database on any cloud anywhere. Describe that. >> So I think, um, you'd be talking about chapter one and two off the cloud, right? When it, when it comes to chapter one in my, in my view, chapter one was very much about attracting people to the cloud by offering them a set of managed services that take away the management headaches and you know, the, the infrastructure, uh, management aspects. Um, but when you think about chapter two, when you think about how to run, uh, mission critical workloads on, on a cloud or on premise, um, you know, you want to have the ability to integrate data States that run in different environments and we think that OpenShift is leveling the playing field by avoiding location, by, by giving clients the ability to basically abstract from PI, Teri cloud infrastructure services and mechanisms. >>And that gives them freedom of action. They can, they can deploy a certain workload in one in one place and then decide six months later that they are better off moving that workload somewhere else. Yes. >> So OpenShift is the linchpin, absolutely. That cross-cloud integration, is that right? Correct. And with the advent of the rise of the operator, I think you see, you know, you see, um, the industry closing the gap between the value proposition of a fully managed service and what a client managed open shift based environment can deliver in terms of automation, simplicity and annual Oh value. Let's talk about the database market and you're trying to, what's happening? You've got, you know, transactional database, you've got analytic database, you've got legacy data warehouses, you've got new, emerging, emerging, you know, databases that are handling unstructured data. You got to know sequel, not only sequel lay out the landscape and where, what's IBM strategy in the database space? >>So our strategy has, has, so starting with the DB to family, right? We have introduced about two one, two years ago we introduced somebody called Tacoma sequel engine. That gives you a consistent, um, experience in from an application and user perspective in the way you consume, um, data for different workload types. Whether that's transactional data, um, analytical use cases, speak data overdue or fast data solution events, different data architectures, everything, you know, with a consistent experience from a management perspective, from a, from a working behavior perspective in the way you interact with, with this as an application. And, and not only that, but also then make that available on premises in the cloud, fully managed or now open shift based on any cloud. Right. So our, our, I would say our commitment right now is very much focusing on leveraging OpenShift, leveraging cloud pick for data as a platform to deliver all these capabilities DB to an open source in a unified and consistent. >>Uh, I would say with a unified and consistent experience on anybody's cloud, it's like what's in any bag was first, you know, like six months ago when we announced it. And I think now for us doing the same with data and making that data, make it easy for people to access state our way every to the sides is really, but Ts, what's IBM's point of view on, on the degree of integration that you have to have in that stack from hardware and software. So people, some people would argue, well you have to have the same control plane, same data plane, same hardware, same software, same database on prem as you have in the cloud. What's your thoughts on that degree of homogeneity that's required to succeed? So I think it's certainly something that, uh, companies strive to get to simplify the data architectures, unify, consolidate, reduced the number of data sources that you have to deal with. >>But the reality is that the average enterprise client has 168 different data services they have to incorporate, right? So to me it's a moving target and while you want to consolidate, you will never fully get there. So I think our approach is we want to give to client choice best different choice in terms of technologies for for the same workload type. Potentially, whether it's a post test for four transactional workloads for TB, two for transactional workloads, whatever fits the bill, right? And then at the same time, um, at the same time abstract or unify on top of that by, by when you think about operators and OpenShift, for instance, we invest in a, in um, in operators leveraging a consistent framework that basically provides, you know, homogeneous set of interfaces by which people could deploy and life cycle manager Postgres instance or DB two instance. >>So you need only one skillset to manage all these different data services and you know, it reduces total cost of ownership is it provides more agility and, and you know, you know, accelerates time to value for this client. So you're saying that IBM strategy recognizes the heterogeneity within the client base, right? Um, you're not taking, even though you might have a box somewhere in the portfolio, but you're not a, you need this box only strategy. The God box. This is, this is the hammer and every opportunity is a nail. Yeah, we have way beyond that. So we, we are much more open in the way we embrace open source and we bring open source technologies to our enterprise clients and we invest in integration of these different technologies so they can, the value of those can be actuated much more in a much more straightforward fashion. >>The thing about cloud pay for data and the ability to access data insights in different open Sozo, different depositories, IBM, one third party, but then make that data accessible through data virtualization or full governance, applying governance to the data so that data scientists can actually get reef that data for, for his work. That is really important. Can you argue that that's less lock-in than say like they say the God box approach or the cloud only approach? Yeah, absolutely. Because how so? How so? Because, well, because we give you choice to begin with, right? And it's not only choice in terms of the data services and the different technologies that are available, but also in terms of the location where you deploy these data services and how you run them. Okay. So to me it's all about exit strategies. If I go down a path and a path doesn't work for me, how do I get out? >>Exactly. Um, is that a concern of customers in terms of risk management? Yeah. I think, look, every, every costume out there, I daresay, you know, has a data strategy and every customer needs to make some decisions. But you know, there's only so much information you have today to make that decision. But as you learn more, your decision might change six months down the road. And you know, how to preserve that agility as a business to do course corrections I think is really important. So, okay, a hypothetical that this happens every day. You've got a big portfolio companies, they've done a lot of M and a, they've got, you know, 10 different databases that they're running. They got different clouds that they're using, they've got different development teams using, using different tooling, certainly different physical infrastructure. And they really haven't had a strategy to bring it all together. >>Uh, you're hired as the, uh, the data architect or the CTO of the company and say, but Tia's, the CEO says, fix this problem. You're not, we're not taking advantage, uh, and leveraging our data. Where do you start? So of course, being IBM, I would recommend to start with clapping for data as the number one data platform out there because eventually every component will want to capitalize on the value that the data represents. It's not just about a data layer is not just about a database, it's about an indicated solutions tech that gets people to do analytics over the data, the life insights from the data. That's number one. But even if you are, you know, if, if, if it's not I the IBM stack, right, I would always recommend to the client to think about a strategy that that allows for the flexibility change to change course wide and move workloads from one location to another or move data from one technology stack to another. >>And I think that that kind of, you know, that agility and flexibility and, um, translate into, um, risk mitigation strategies that every client should think about. So cloud pack for data, it's okay, let's start there. I'm gonna, I'm gonna, I'm gonna install that, or I'm gonna access that in, into the cloud. And then what do I have to do as a customer to take advantage of that? Do I just have to point it at my data stores? What are the prerequisites? Yeah. So let's say you deploy that on IBM cloud, right? Then you have, you usually are invested already. So you have data, large data estates either residing on share is already in the cloud. You can pull those, those, those datasets in remotely without really moving the workload of the data sets into a cloud pixel, data managed environment by using technologies like data virtualization, right? >>Or using technologies like data stage and ETL capabilities, um, to access the data. But you can also, as you modernize and you build your next next generation application, you can do that within that managed environment with OpenShift. And, and that's what most people want to do. They want to do a digital transformation. They want to modernize the workloads, but we want to leverage the existing investments that they have been making over the last decade. Okay. So, but there's a discovery phase, right, where you bring in cloud pack for data to say, okay, what do I have? Yup, go find it. And then it's bringing in necessary tooling on the, on the diff with the development side with things like OpenShift and then, and then what it's magically virtualizes my data is that, so just on that point, I think you know, the, what made us much more going forward for his clients is how they can incorporate different datasets with adding insure in open source technologies or, or DB two on a third party vendor said, I don't want to mention right now, but, but what matters more is, so how do I make data accessible? >>How do I discover the data set in a way that I can automatically generate metadata? So I have a business glossary, I have metadata and I understand various data sets. Lyft, that's their vision objective business technology objectives. To be able to do that and to what's watching knowledge catalog, which is part of topic for data is a core component that helps you with dead auto discover the metadata generation basically generating, okay, adding data sets in a way that they are now visible to the data scientists and the ultimate end user. What really matters and I think what is our vision overall is the ability to service the ultimate end user medicine developer, a data scientist, so business analysts so that they can get a chip done without depending on it. Yeah, so that metadata catalog is part of the secret sauce that'll that that allows the system to know what data lives, where, how to get to it and and how to join it. >>Since one of the core elements of that, of that integrated platform and solution state board. What I think is really key here is the effort we spend in integrating these different components so that it is, it is, it looks seamlessly, it is happening in an automated fashion that as much as possible and it delivers on that promise of a self service experience for that person that sits at the very end of that. Oh, if that chain right, but to your sex so much for explaining that QA for coming on the cube. Great to meet you. All right. Keep it right there everybody. We'll be back with our next guest right after this short break. You're watching the cube from the IBM data and AI forum in Miami. We'll be right back.