>>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.

(upbeat music) >> Hello everyone, and welcome to this CUBE conversation where we're going to go deep into system performance. We're here with an expert. Kim Leyenaar is the Principal Performance Architect at Broadcom. Kim. Great to see you. Thanks so much for coming on. >> Thanks so much too. >> So you have a deep background in performance, performance assessment, benchmarking, modeling. Tell us a little bit about your background, your role. >> Thanks. So I've been a storage performance engineer and architect for about 22 years. And I'm specifically been for abroad with Broadcom for I think next month is going to be my 14 year mark. So what I do there is initially I built and I manage their international performance team, but about six years ago I moved back into architecture, and what my roles right now are is I generate performance projections for all of our next generation products. And then I also work on marketing material and I interface with a lot of the customers and debugging customer issues, and looking at how our customers are actually using our storage. >> Great. Now we have a graphic that we want to share. It talks to how storage has evolved over the past decade. So my question is what changes have you seen in storage and how has that impacted the way you approach benchmarking. In this graphic we got sort of big four items that impact performance, memory processor, IO pathways, and the storage media itself, but walk us through this data if you would. >> Sure. So what I put together is a little bit of what we've seen over the past 15 to 20 years. So I've been doing this for about 22 years and kind of going back and focusing a little bit on the storage, we looked back at hard disk, they ruled for. And nearly they had almost 50 years of ruling. And our first hard drive that came out back in the 1950s was only capable of five megabytes in capacity. and one and a half iOS per second. It had almost a full second in terms of seat time. So we've come a long way since then. But when I first came on, we were looking at Ultra 320 SCSI. And one of the biggest memories that I have of that was my office is located close to our tech support. And I could hear the first question was always, what's your termination like? And so we had some challenges with SCSI, and then we moved on into SAS and data protocols. And we continued to move on. But right now, back in the early 2000s when I came on board, the best drives really could do maybe 400 iOS per second. Maybe two 250 megabytes per second, with millisecond response times. And so when I was benchmarking way back when it was always like, well, IOPS are IOPS. We were always faster than what the drives to do. And that was just how it was. The drives were always the bottleneck in the system. And so things started changing though by the early 2000s, mid 2000s. We started seeing different technologies come out. We started seeing that virtualization and multi-tenant infrastructures becoming really popular. And then we had cloud computing that was well on the horizon. And so at this point, we're like, well, wait a minute, we really can't make processors that much faster. And so everybody got excited to include (indistinct) and the home came out but, they had two cores per processor and four cores per processor. And so we saw a little time period where actually the processing capability kind of pulled ahead of everybody else. And memory was falling behind. We had good old DVR, 2, 6, 67. It was new with the time, but we only had maybe one or two memory channels per processor. And then in 2007 we saw disk capacity hit one terabyte. And we started seeing a little bit of an imbalance because we were seeing these drives are getting massive, but their performance per drive was not really kind of keeping up. So now we see a revolution around 2010. And my co-worker and I at the time, we have these little USB discs, if you recall, we would put them in. They were so fast. We were joking at the time. "Hey, you know what, wonder if we could make a raid array out of these little USB disks?" They were just so fast. The idea was actually kind of crazy until we started seeing it actually happen. So in 2010 SSD started revolutionizing storage. And the first SSDs that we really worked with these plaint LS-300 and they were amazing because they were so over-provisioned that they had almost the same reader, right performance. But to go from a drive that could do maybe 400 IOS per second to a drive like 40,000 plus iOS per second, really changed our thought process about how our storage controller could actually try and keep up with the rest of the system. So we started falling behind. That was a big challenge for us. And then in 2014, NVMe came around as well. So now we've got these drives, they're 30 terabytes. They can do one and a half million iOS per second, and over 6,000 megabytes per second. But they were expensive. So people start relegating SSDs more towards tiered storage or cash. And as the prices of these drives kind of came down, they became a lot more mainstream. And then the memory channels started picking up. And they started doubling every few years. And we're looking now at DVR 5 4800. And now we're looking at cores that used to go from two to four cores per processor up to 48 with some of the latest different processes that are out there. So our ability to consume the computing and the storage resources, it's astounding, you know, it's like that whole saying, 'build it and they will come.' Because I'm always amazed, I'm like, how are we going to possibly utilize all this memory bandwidth? How are we going to utilize all these cores? But we do. And the trick to this is having just a balanced infrastructure. It's really critical. Because if you have a performance mismatch between your server and your storage, you really lose a lot of productivity and it does impact your revenue. >> So that's such a key point. Pardon, begin that slide up again with the four points. And that last point that you made Kim about balance. And so here you have these, electronic speeds with memory and IO, and then you've got the spinning disc, this mechanical disc. You mentioned that SSD kind of changed the game, but it used to be, when I looked at benchmarks, it was always the D stage bandwidth of the cash out to the spinning disc was always the bottleneck. And, you go back to the days of you it's symmetrics, right? The huge backend disk bandwidth was how they dealt with that. But, and then you had things the oxymoron of the day was high spin speed disks of a high performance disk. Compared to memories. And, so the next chart that we have is show some really amazing performance increases over the years. And so you see these bars on the left-hand side, it looks at historical performance for 4k random IOPS. And on the right-hand side, it's the storage controller performance for sequential bandwidth from 2008 to 2022. That's 22 is that yellow line. It's astounding the increases. I wonder if you could tell us what we're looking at here, when did SSD come in and how did that affect your thinking? (laughs) >> So I remember back in 2007, we were kind of on the precipice of SSDs. We saw it, the writing was on the wall. We had our first three gig SAS and SATA capable HPAs that had come out. And it was a shock because we were like, wow, we're going to really quickly become the bottleneck once this becomes more mainstream. And you're so right though about people work in, building these massive hard drive based back ends in order to handle kind of that tiered architecture that we were seeing that back in the early 2010s kind of when the pricing was just so sky high. And I remember looking at our SAS controllers, our very first one, and that was when I first came in at 2007. We had just launched our first SAS controller. We're so proud of ourselves. And I started going how many IOPS can this thing, even handled? We couldn't even attach enough drives to figure it out. So what we would do is we'd do these little tricks where we would do a five 12 byte read, and we would do it on a 4k boundary, so that it was actually reading sequentially from the disc, but we were handling these discrete IOPS. So we were like, oh, we can do around 35,000. Well, that's just not going to hit it anymore. Bandwidth wise we were doing great. Really our limitation and our bottleneck on bandwidth was always either the host or the backend. So, our controllers are there basically, there were three bottlenecks for our storage controllers. The first one is the bottleneck from the host to the controller. So that is typically a PCIe connection. And then there's another bottleneck on the controller to the disc. And that's really the number of ports that we have. And then the third one is the discs themselves. So in typical storage, that's what we look at. And we say, well, how do we improve this? So some of these are just kind of evolutionary, such as PCIE generations. And we're going to talk a little bit about that, but some of them are really revolutionary, and those are some of the things that we've been doing over the last five or six years to try and make sure that we are no longer the bottleneck. And we can enable these really, really fast drives. >> So can I ask a question? I'm sorry to interrupted but on these blue bars here. So these all spinning disks, I presume, out years they're not. Like when did flash come in to these blue bars? is that..you said 27 you started looking at it, but on these benchmarks, is it all spinning disc? Is it all flash? How should we interpret that? >> No, no. Initially they were actually all hard drives. And the way that we would identify, the max iOS would be by doing very small sequential reads to these hard drives. We just didn't have SSDs at that point. And then somewhere around 2010 is where we.. it was very early in that chart, we were able to start incorporating SSD technology into our benchmarking. And so what you're looking at here is really the max that our controller is capable of. So we would throw as many drives as we could and do what we needed to do in order to just make sure our controller was the bottleneck and what can we expose. >> So the drive then when SSD came in was no longer the bottleneck. So you guys had to sort of invent and rethink sort of how, what your innovation and your technology, because, I mean, these are astounding increases in performance. I mean, I think in the left-hand side, we've built this out pad, you got 170 X increase for the 4k random IOPS, and you've got a 20 X increase for the sequential bandwidth. How were you able to achieve that level of performance over time? >> Well, in terms of the sequential bandwidth, really those come naturally by increases in the PCIe or the SAS generation. So we just make sure we stay out of the way, and we enable that bandwidth. But the IOPS that's where it got really, really tricky. So we had to start thinking about different things. So, first of all, we started optimizing all of our pathways, all of our IO management, we increased the processing capabilities on our IO controllers. We added more on-chip memory. We started putting in IO accelerators, these hardware accelerators. We put in SAS poor kind of enhancements. We even went and improved our driver to make sure that our driver was as thin as possible. So we can make sure that we can enable all the IOPS on systems. But a big thing happening a few couple of generations ago was we started introducing something called tri capable controllers, which means that you could attach NVMe. You could attach SAS or you could attach SATA. So you could have this really amazing deployment of storage infrastructure based around your customized needs and your cost requirements by using one controller. >> Yeah. So anybody who's ever been to a trade show where they were displaying a glass case with a Winchester disc drive, for example, you see it's spinning and its actuators is moving, wow, that's so fast. Well, no. That's like a tourist slower. It's like a snail compared to the system's speed. So it's, in a way life was easy back in those days, because when you did a right to a disk, you had plenty of time to do stuff, right. And now it's changed. And so I want to talk about Gen3 versus Gen4, and how all this relates to what's new in Gen4 and the impacts of PCIe here, you have a chart here that you've shared with us that talks to that. And I wonder if you could elaborate on that, Kim. >> Sure. But first, you said something that kind of hit my funny bone there. And I remember I made a visit once about 15 or 20 years ago to IBM. And this gentleman actually had one of those old ones in his office and he referred to them as disk files. And he never until the day he retired, he'd never stopped calling them disc files. And it's kind of funny to be a part of that history. >> Yeah. DASD. They used to call it. (both laughing) >> SD, DASD. I used to get all kinds of, you know, you don't know what it was like back then, but yeah. But now nowadays we've got it quite easily enabled because back then, we had, SD DASD and all that. And then, ATA and then SCSI, well now we've got PCIe. And what's fabulous about PCIe is that it just has the generations are already planned out. It's incredible. You know, we're looking at right now, Gen3 moving to Gen4, and that's a lot about what we're going to be talking about. And that's what we're trying to test out. What is Gen4 PCIe when to bias? And it really is. It's fantastic. And PCIe came around about 18 years ago and Broadcom is, and we do participate and contribute to the PCIe SIG, which is, who develops the standards for PCIe, but the host in both our host interface in our NVMe desk and utilize the standards. So this is really, really a big deal, really critical for us. But if you take a look here, you can see that in terms of the capabilities of it, it's really is buying us a lot. So most of our drives right now NVMe drives tend to be by four. And a lot of people will connect them. And what that means is four lanes of NVMe and a lot of people that will connect them either at by one or by two kind of depending on what their storage infrastructure will allow. But the majority of them you could buy, or there are so, as you can see right now, we've gone from eight gig transfers per second to 16 gig of transfers per second. What that means is for a by four, we're going from one drive being able to do 4,000 to do an almost 8,000 megabytes per second. And in terms of those 4k IOPS that really evade us, they were really really tough sometimes to squeeze out of these drives, but now we're got 1 million, all we have to 2 million, it's just, it's insane. You know, just the increase in performance. And there's a lot of other standards that are going to be sitting on top of PCIe. So it's not going away anytime soon. We've got to open standards like CXL and things like that, but we also have graphics cards. You've got all of your hosts connections, they're also sitting on PCIe. So it's fantastic. It's backwards, it's orbits compatible, and it really is going to be our future. >> So this is all well and good. And I think I really believe that a lot of times in our industry, the challenges in the plumbing are underappreciated. But let's make it real for the audience because we have all these new workloads coming out, AI, heavily data oriented. So I want to get your thoughts on what types of workloads are going to benefit from Gen4 performance increases. In other words, what does it mean for application performance? You shared a chart that lists some of the key workloads, and I wonder if we could go through those. >> Yeah, yeah. I could have a large list of different workloads that are able to consume large amounts of data, whether or not it's in small or large kind of bytes of data. But as you know right now, and I said earlier, our ability to consume these compute and storage resources is amazing. So you build it and we'll use it. And the world's data we're expected to grow 61% to 175 zettabytes by the year 2025, according to IDC. So that's just a lot of data to manage. It's a lot of data to have, and it's something that's sitting around, but to be useful, you have to actually be able to access it. And that's kind of where we come in. So who is accessing it? What kind of applications? I spend a lot of time trying to understand that. And recently I attended a virtual conference SDC and what I like to do when I attend these conferences is to try to figure out what the buzz words are. What's everybody talking about? Because every year it's a little bit different, but this year was edge, edge everything. And so I kind of put edge on there first in, even you can ask anybody what's edge computing and it's going to mean a lot of different things, but basically it's all the computing outside of the cloud. That's happening typically at the edge of the network. So it tends to encompass a lot of real time processing on those instant data. So in the data is usually coming from either users or different sensors. It's that last mile. It's where we kind of put a lot of our content caching. And, I uncovered some interesting stuff when I was attending this virtual conference and they say only about 25% of all the usable data actually even reach the data center. The rest is ephemeral and it's localized, locally and in real time. So what it does is in the goal of edge computing is to try and reduce the bandwidth costs for these kinds of IOT devices that go over a long distance. But the reality is the growth of real-time applications that require these kinds of local processing are going to drive this technology forward over the coming years. So Dave, your toaster and your dishwasher they're, IOT edge devices probably in the next year, if they're not already. So edge is a really big one and consumes a lot of the data. >> The buzzword does your now is met the metaverse, it's almost like the movie, the matrix is going to come in real time. But the fact is it's all this data, a lot of videos, some of the ones that I would call out here, you mentioned facial recognition, real-time analytics. A lot of the edge is going to be real-time inferencing, applying AI. And these are just a massive, massive data sets that you again, you and of course your customers are enabling. >> When we first came out with our very first Gen3 product, our marketing team actually asked me, "Hey, how can we show users how they can consume this?" So I actually set up a head to environment. I decided I'm going to learn how to do this. I set up this massive environment with Hadoop, and at the time they called big data, the 3V's, I don't know if you remember these big 3Vs, the volume, velocity and variety. Well Dave, did you know, there are now 10 Vs? So besides those three, we got velocity, we got valued, we got variability, validity, vulnerability, volatility, visualization. So I'm thinking we need just to add another beat of that. >> Yeah. (both laughing) Well, that's interesting. You mentioned that, and that sort of came out of the big data world, a dupe world, which was very centralized. You're seeing the cloud is expanding, the world's getting, you know, data is by its very nature decentralized. And so you've got to have the ability to do an analysis in place. A lot of the edge analytics are going to be done in real time. Yes, sure. Some of it's going to go back in the cloud for detailed modeling, but we are the next decade Kim, ain't going to be like the last I often say. (laughing) I'll give you the last word. I mean, how do you see this sort of evolving, who's going to be adopting this stuff. Give us a sort of a timeframe for this kind of rollout in your world. >> In terms of the timeframe. I mean really nobody knows, but we feel like Gen5, that it's coming out next year. It may not be a full rollout, but we're going to start seeing Gen5 devices and Gen5 infrastructure is being built out over the next year. And then follow very, very, very quickly by Gen6. And so what we're seeing though is, we're starting to see these graphics processors, These GPU's, and I'm coming out as well, that are going to be connecting, using PCIe interfaces as well. So being able to access lots and lots and lots of data locally is going to be a really, really big deal and order because worldwide, all of our companies they're using business analytics. Data is money. And the person that actually can improve their operational efficiency, bolster those sales and increase your customer satisfaction. Those are the companies that are going on to win. And those are the companies that are going to be able to effectively store, retrieve and analyze all the data that they're collecting over the years. And that requires an abundance of data. >> Data is money and it's interesting. It kind of all goes back to when Steve jobs decided to put flash inside of an iPhone and the industry exploded, consumer economics kicked in 5G now edge AI, a lot of the things you talked about, GPU's the neural processing unit. It's all going to be coming together in this decade. Very exciting. Kim, thanks so much for sharing this data and your perspectives. I'd love to have you back when you got some new perspectives, new benchmark data. Let's do that. Okay. >> I look forward to it. Thanks so much. >> You're very welcome. And thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, we're now going >>to explore what it's like to be the CEO of a fast paced growth company in Silicon Valley. And how the cloud, however, you wanted to find the cloud public cloud on Prem Hybrid, etcetera. How it supported that growth. And with me is Kathy Southwick, who is the CEO of pure storage. Kathy is really deep experience. Managing technology organizations spent a number of years overseeing A T and T s cloud planning and engineering and another few years overseeing a team of a Couple 1000 network and I T engineers working to break the physical stranglehold of fossilized telco networks, implementing network functions, virtualization and a software defined methodology for the company. And, of course, you spent the last couple of years is the CEO of Pure. So Cathy, it's great to see you again. Thank you for coming on the program. >>Thanks for having me. It's good to be here. >>You're very welcome. And so so >>given your >>experience with cloud, you know, dating back to really the early part of last decade. How did you look at cloud back then and how How is it evolved from your point of view? >>You know, it's Ah, it's an interesting question because I think that we've there's some things that have moved very fast and there's some some things that are very much the same as they were even a decade ago. I think that all companies are very focused on How do you think about Cloud? Do you think about it as on Prem? And when I started, we really were focused on an on Prem solution, and I'm in building an on Prem private cloud to help modernize our business. So I think that, you know, with that all companies are still in that same mindset of how do I want to think about Cloud? And how do I want to think about that on Prem versus Public versus, you know, combination or some type of hybrid solution? So I think all of us around that journey, it just seems like it's taken. It's probably a bit longer than most of us probably thought from beginning. >>So as a CEO thinking about that evolution, how has that informed the way you think about applying specifically the public cloud to pure business. >>You know, I think that we've been a for pure ourselves. I think we're in a really unique position. We were essentially born in the cloud. So we're, you know, company. That's 10 11 years old. And if I If I give the contrast of that of 18 t being, you know, 130 year old company Onda having a lot of applications that have, you know, lived historically on prim. There's very different issues and challenges that you have pure has had that. I think the advantage just like many other companies that were born in the cloud who have can see what advantages are very quickly. And we made decisions early on that said that we were gonna actually do both. We were gonna look to say, How do I put those applications in that in that data, whether it was on public or in on Prem and be able to do that both in the i t. Side as well as within the product side? So how we build our products now, >>as I mentioned up front, you have obviously a lot of experience managing large technology teams. My question is. When you first saw the emergence of the modern cloud, how did you communicate with your team members? I mean, you mentioned you were kind of building your own private cloud, so I guess that's less threatening to people. But what was it like? You know, Was there a concern? You know, with the eager to jump in? What was that dynamic like? And how did you manage >>it? You know, it's really it's a different depending on the different part of the organization. So I'll give you kind of two things I learned one of them was that our teams in the operation side, they saw it as a huge advantage. They saw it as an opportunity to really modernized to really get themselves both their own individual skill sets advanced, as well as provide a better level of service for our internal, you know, customer, so to speak. Our application in our data partners that we had to work with, um, they thought is an opportunity to bring agility to their applications quicker speed to market, um, or currency of their applications. So they actually got some benefits that they weren't. Actually, I'll call planning for they were they had the opportunity toe get investment in their applications without having to put the that investment on themselves. I would tell you the thing I learned from the teams, this is probably might be a little bit surprised. But often, you know, leaders believe like, you gotta have all the answers. You're gonna drive everything you're gonna let make sure everyone knows what needs to get done and what I actually found. This was actually one of my big moments, I think, was our Our individuals are employees are teams. They're so brilliant and so bright on driving change. And a lot of times leaders, I think, get in the way that so for cloud and adoption, it was really about me getting out of the way. It was really about setting that north star for where we want to go from the ability to deliver fast and quick for our business. And they get out of the way and let our teams actually drive. So it was a great, um, it was we actually actually saw the reverse. I saw more employees wanting to drive, and I needed to, like, back out and just say, Here's what we need to go. Let them drive us there. >>Alright, So I gotta ask you don't Please don't hate me for asking this question, but was your your gender and advantage was at a disadvantage. It wasn't really irrelevant in that regard. >>It was a relevant um, I think that it was I actually I truly believe it's irrelevant. I think it was literally recognizing that leaders need to set vision and what we want to achieve and let our letter of teams help us drive to get there. And I think that that is, you know, gender neutral. I think it's really about, you know, kind of checking your ego and everything else out to the side. And it's really about empowering people in our teams. Thio help drive us there. >>So thinking about that that learning specifically are there any similar tectonic shifts that you're you're seeing today where you can apply that experience? I'm just like, for instance, new modes of application development and requiring new skill sets are, or maybe another that you can think of. >>Yeah, I think I think honestly, it traverse is everything that we that we have to do as a you know, as a leader of a technology team, and whether you're in a high growth company like Pure or you're in a company that's trying to take costs out of your business or trying to, you know, do things. I think that it, um it really is a matter of leaders needing to set the stage. And so if we're trying to drive, you know, changing the business, it's really making sure that we're doing I'll calm or more empowering of our employees and they because they will see the way that we can get there. It's just a matter of, you know, letting them have that ability to do it. >>So you joined pure around two years ago and obviously growing very quickly. I love pandemic has changed the trajectory of that growth, but still good outlook. Um, but Silicon Valley fast paced company, you know, I kind of put it in the camp of the the work days, and the service now is that could have similar similar cultural patterns there. So you talked a little bit about this, but I wonder if we could come back and more specifically how you're leveraging cloud, how you're thinking about it, you know, on Prem Hybrid, Now the edge. And how did that contribute Thio Puros growth? >>Yeah, that za great question because I think that why I shared earlier, you know, we were essentially born in the cloud. I think that what it's really driven us is to be thinking more forward about where customers were going and what their challenges are. So whether it's for the I t. Teams on what we're trying to do to deliver for our business and, you know, innovation, they're obviously trying to make sure they can hit their revenue goals and all those things that important that every business deals with. But we also have that same mindset on how we develop our products. So it's really all driven by where the customer is going that they need data mobility. They need application mobility. They need really portability so that the moment that you have that ability where you can kind of control your destiny and define it, and you only could get that by having, you know, applications that are portable and data that is mobile and secure, that you have that kind of flexibility. So I think for pure we've been definitely in a great position to drive for our customers or drive where our customers are going. And so we have to find our entire product set. So not just how we operate as a business and run our business. But then how we define for our customers Same mindset is if our customers are going to the cloud that we need, have products that can help them to be in the cloud or be, you know, on print and let them decide what that looks like. Well, >>it's interesting you mentioned that and I hearken back to the The Port Works acquisition, which is an attempt to really change the way application development has done is another sort of approach Thio in a sort of modern data architecture, you, as the CEO of a technology company, most CEO, is that I know inside the tech companies that they're sort of the dog Fuding or champagne drinking, you know, testing. So So had you already started to sort of use that tech? Are you starting to, you know, Does it support that vision that you just put forth? Maybe you could talk a little bit about that. >>Yeah, It does. So we eso We had not been using port works as a za product. We were just starting down that path of looking at How do we do container ization for the applications that we do have on Prem? That's both in our engineering side as well as within I t. And so But we quickly have recognized, just like you know, And part of that acquisition is applications or companies won't have the ability to have that portability of their applications and have that flexibility that they're all striving for unless they've done things like containerized or applications made them that they're able to move them across different cloud environments, whether that's on Prem or off Prem or some hybrid eso for ourselves. You know, Port Works was a really critical acquisition, will help us on our own journey of doing the application, modernization and putting that keep those capabilities in place. But it will also enable our customers to have that same flexibility. So, again, going back to the we've adopt, these things aren't like a this is for this group, and this is for you know, this customer. It's really about how we operate both internally and then what we are providing for our customers so that portability and being able to have control of your own destiny, that's that's really to me what hybrid cloud is all about. And you can't really achieve that If you don't have some of these capabilities within your, you know, within kind of your toolbox. >>Great. Thank you for that. So I'm interested in is the head of, ah technology group at a tech company? And what are the meaningful differences? I mean, a lot of differences, but relative to CEO of a large telco or or other incumbent, you know, what are some of the good, the bad? And, uh, you know, the ugly, the differences. >>Yeah, you know, it's I meet with a lot of CEOs across Silicon Valley and we kind of joked that when you are working in a company that is a technology based company, you know, everybody knows how to dio, you know, because you do you have a brilliant engineers and and that they do know. I think the difference that you start to see is that you know, I t is, um is required to make sure that availability is their inherent in what you're doing on immediate roll out with like, you know, an application that's occurring. That's very different than how you do product lifecycle management. Um, what what we've what I've seen, actually, though, is more similarities. I know that's probably surprised to you, but coming out of a T and T, what I have been working on those last couple of years was actually doing the combination of engineering and I t into one organization and that you do have a lot of benefits for, for how you can then develop, how you can manage and the skill sets. There's a lot of similarities. So there's there's actually probably more similarities between companies and on what they're trying to achieve than than you would probably think there would be just because we're all trying to make sure that we can develop quickly. How about is >>it relates to cloud Cathy? I mean, I remember the early days of cloud, a lot of the big banks that we could build our own cloud. We can essentially compete at scale with with Amazon, where you know the big bank on. Then I think they quickly realized well, the economics actually don't favor us necessarily. Do you think there's a different perception about the use of cloud between sort of traditional incumbents and a tech company in Silicon Valley? And if so, how? >>So now I think that the if you are, you know, a bank is you refer to, and having it really is where you're starting from. If you have a very large infrastructure footprint and application footprint, your applications probably not born in the cloud. There's a lot of modernization that has to be done with those applications so that they could operate as efficiently in a public cloud as an example. And I think that's something that sometimes gets overlooked is there are enormous benefits going to public cloud. But there's also cost if your applications or your data doesn't really fit as well in that type of environment. So I think that for large enterprises like the banks, some of the telcos they've got very large footprints of infrastructure. Already, those investments have been made, and what they're really looking for is how doe I increase my ability to, you know, whether it's agility or its speed, or it's lower cost or it's all those things, and I think that's the That's a different path of different journey that they're on. So they're trying to balance all those equations of, you know, the economics as well as the ability to have, you know, no more investment or minimal investment in that infrastructure. For companies like Pure, where we started off of those investments are decision and kind of. The decision tree that we use is if it makes sense. And I don't have to make that investment on Prem for whatever reason, that I should go ahead and make that investment in a public cloud strategy or a hybrid cloud strategy kind. Differentiate that because I think that it's different depending on the company. You are, um, and so it really kind of depends on where you're starting from then. It also depends on what you're trying to achieve if you're just trying to achieve an economic solution. If you're trying to achieve a strategic solution, if you're trying to get agility. Andi, I think it is different for companies, and it's different depending where you're at in your kind of journey. So for a Silicon Valley company whose you know hyper growth, you know, one. We're very focused on abilities. You know everything from scale, because we've got to scale quickly. And those are things that we don't wanna have to start going and building all these data centers to go do that. We don't have those embedded investments. So it's Ah, it's a real difference in where your starting point is. And I think there I think there's value in in all those different type of approaches, >>right? And it's a real advantage for you that you don't have to shell out all that cap ex on Data Center. >>That's right. Um, as you look >>back at the last 10 years of cloud, you know, it was largely about eliminating the heavy lift of infrastructure deployment and SAS if I ng you know the business, what do you see? Going forward? What do you think the was gonna unfold in the 2020 is? Is it gonna be more of the same? Or do you expect meaningful differences? >>I think that we're going to get better as, um as you know, technology leaders on how to quickly make decisions. Um, and not its have it less political. And I think Kobe is actually taught us a lot about that around companies more willing to make. I'll call it a A you know, a faster decision and remove some of the red tape. I've heard this from many of my peers that things that might have taken them months and months to get approved. Um, it's nowadays if even if they even have to go get approval. So I think that what we're going to see is we'll see the continuance of, um, you know, a public and I'll call really hybrid cloud type of solutions. And I think it will be more purposeful about what goes there and how. How that can help us toe, you know, I'll call it enable us much faster than we've been able to do it before. I think that's been our challenges. We've, you know, we get mired into some of the you know, the details of some of these things that maybe it would be easier for us to just make the decision to move forward than Thio. Keep going around around on what's the right way to do it. Yeah, >>so that's interesting. You're saying about the fast decisions? I felt like, ah, lot of 2020 was very tactical. Okay, go deal with the work from home, etcetera. Although you you definitely see I t spending, uh, suppressed in 2020. Our forecast was minus 4% but we're saying it's gonna grow. We actually see a decent snapback. You know, what are you seeing? Generally, Not even necessarily pure. But when you talk to some of your colleagues, you obviously in the technology business, it's good to be in the technology business these days. But to use do you see spending, you know, generally coming back And maybe the timing first half, maybe a little soft second. What are you seeing >>there? Yeah, almost identical wage that. I think that we'll see, you know, a little bit of, ah tendency toe, not really hold back, but really kind of see what's happening in the first quarter of the year. There's a lot, you know, going on with companies and everyone's having to kind of balance at what that looks like. I do see. And what I'm hearing from several of my peers is that, you know, it's not necessarily budget cuts. It might be budget re directions. It might be rude prioritization, but definitely technology investments are still there, and it's still important for businesses to keep on their journeys on. But we do see that even at pure as a way to differentiate ourselves in the market as well, do you? What >>about the work from home piece? I mean, prior to co vid, I think the average was about 15 or 16% of employees work from home. You know, now it's gotta be, you know, well, over in the high seventies, Onda CEO is that we've talked to suggest that, you know, that's gonna come down in the first half, maybe down toe, still pretty high 50 60%. But then eventually is gonna settle at a higher rate than it was pre pre covert. Maybe double that rate may be in the 30 35 maybe even 40%. You know? What are you expecting >>Something probably very similar. I think that what companies have recognized and I actually tell you CEO have thought this many of them for many years that there is a huge value value and having some type of hybrid model. There's value in having, you know, both from a business perspective as well as a personal perspective. So employees work life balance and trying to balance that. So I think that, you know, we a pure and myself, As you know the CEO hugely expect that we will see some type of you know, I'll call leveling off, figure out what's the right for the right group. And I think what we don't want to get into is, you know, Chris prescriptive that says, You know, this is what the company will look like as a whole. I think it really is going to come down to certain certain types of work are more conducive to a more work, remote environment others need to have. And I always kind of uses term of individual, you know, productivity versus team. You know, productivity. We've seen, you know, great advances and or individual productivity. A team productivity is still a challenge when you're still trying to do very collaborative, you know, brainstorming sessions. And so we are looking at capabilities to be able to enable our employees to do that. But there there's some things you just can't replace. The human interaction and ability to very quickly inter actively, you know, five minutes catch someone to do that. So I think we'll see. We'll see both. We'll see some leveling off, and I think we'll see some areas of businesses that have once thought You can't do that remote. They might actually say, Hey, that is work that commute remote So I think we'll see a combination of both. That's an >>interesting perspective on productivity. And what's the What's the old saying is You could go go faster alone. But further as a team and and not a lot of folks have been talking about that team productivity, we we clearly saw the hit the positive hit on productivity, especially in the in the technology business. So So my question then is so you expect? You know H Q doesn't go away. Maybe it gets, you know, maybe it gets smaller, Uh, but so is their pent up demand for technology spending at the headquarters. Because you've been you've been, you know, pushing tech out out to the edge out to the remote workers. Securing those remote workers figuring out better ways to collaborate is their pent up demand at H. Q. >>Um, absolutely. We've been, you know, we've been actually exploring different technologies. We've been uh, looking at what are things that you know could help create a different kind of experience, eh? So I do think it will be some different types of technology. Those would be the things that maybe aren't even out there developed yet on Have you create some of those comparable experiences. So I think that the notion of you know individuals will continue to thrive, but we've got to start working on How do we continue to enhance that? That team, um, collaborative productivity environment that looks and feels different than what it might look like today. Yeah. >>They got to leave it there. Great as always. Having you in the Cube. Thanks so much for participating in Cuban Cloud. >>Great. It's great to be here. Thank you. >>Keep it right there. Back more content right after this short break. >>Yeah.

>> Announcer: From around the globe. It's theCUBE with digital coverage of AWS re:Invent 2020, sponsored by Intel, AWS and our community partners. (upbeat music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're coming to you from our Palo Alto studios today with our ongoing coverage of AWS re:Invent 2020. It's a virtual event, like all the events in 2020, but we've been going there since 2013. We're happy to be back this year and we're excited to have for the first time on theCUBE, our next guest, she's Kelly Herod the US SAP Offering Leader for Deloitte Consulting. Kelly, great to see you. >> Great to see you as well, Jeff, thanks so much for having me. >> Absolutely, so first time on, on theCUBE, you guys have a really interesting concept at Deloitte, you call it the Kinetic Enterprise. What is the Kinetic Enterprise all about? >> Yes. So if you think about the past, organizations built their technology infrastructures to be what we would call built to last, the future though is all about built to evolve. And that's exactly what the Kinetic Enterprise is. It's really how we're helping our clients create the right technology infrastructures that evolve with their business. And Kinetic Enterprise is focused on four key pillars. The first, that we're building a technology solution that's clean. That means we want to have reduced amount of custom code or things that we may have built that really rack up your technical debt. The second pillar is that it's intelligent. So we're leveraging all of the technologies, artificial intelligence, machine learning, to really automate and change the way in which an organization runs their business. The third pillar is that it's responsive, and that means it's on the cloud and this is where AWS comes in. And then the last pillar is that it's inclusive. So it uses all of the technologies and microservices available to really optimize and achieve a company's business value objectives. >> So that is a great summary, and I've got the list of the four pillars. It's just interesting you lead with clean. You know, there's a lot of conversation about digital transformation and move fast and be dynamic, you know, would be kind of an opposite to static. But clean, interesting choice of words. It runs with core... Core clean ERP with minimum technical debt. Why clean is such an important thing? I get kind of intelligent and responsive, but clean is an interesting attribute to pick. >> Absolutely, so if you take a step back and think... (Kelly hangs) when comes to ERPs, when ERPs came out, there was... (Kelly hangs) how you're going to run your entire organization on this one solution. What we've found is that as companies have put ERPs, they've gone through and created so much customization, that it's that which makes it very difficult to be able to keep up with technology changes or actually migrate to the next versions. So the concept here is if you're going to go in and put in brand new ERP, such as an SAP S/4HANA, this time around in order to achieve the promise of ERPs, let's make it clean. Let's stick to as much standard functionality as possible within the core, and then we innovate on the edges. And so that will allow us in the future to maintain that flexibility or dynamicism of a Kinetic Enterprise. >> Right. So I have to tease you Kelly 'cause SAP R/3 and ERP is not necessarily synonymous with digital transformation, speed, agility, and embracing change. So you've been involved in Deloitte's SAP practice for a long time. Why should people start to rethink about SAP in terms of being responsive, in terms of being able to change quickly and to your vocabulary, more kinetic? >> And you're right. You know, I've been doing SAP for 20 years. So I actually did start back in the R/3 days. And, you know, I would just say that things are changing, is evolving. You know, SAP themselves has been going through a transformation, a revolution. You look at the ERP landscape as a whole, all of the ERP players are moving to the cloud. The technology is the backbones are changing. Now the reality is, you know, going in and actually changing out your ERP, no matter what solution you're using, it's a big endeavor or undertaking. The goal here, and why we're partnering with SAP, partnering with AWS is really focused on how can we make this more efficient for our clients? More importantly, I like to think about it as how can we make this less of a one and done, and more of a let's keep transforming the technologies and the business as things are changing in the market, along the way. And using technologies to even change how we implement, allows us to do that. >> So, Kelly, another thing a lot of people probably don't think of is SAP and AWS, together in the same sentence. So I'm sure there's a lot of people that are much more intelligent about this, but for those that aren't as familiar, tell us a little bit about the relationship with SAP and AWS and then how you guys are leveraging that at Deloitte. >> Absolutely. So when you... There's a couple of things that I would bring up. One is SAP S/4HANA solutions, in particular, but any SAP environment that you're running on, one of the objectives most of our clients are focused on is how to move to the cloud, and that's where AWS comes in. You can absolutely run any of your SAP solutions on AWS. And what that brings you with is more flexibility, so that you can actually scale or contract your infrastructure that you're running SAP on based on your business needs. The second thing that we've been partnering with AWS to do is a little bit of what I just mentioned, which was a teaser around, how do you change the way you even go about implementing an SAP solution or start to migrate your business? So one of the things we asked ourselves was, could we radically change how you jumpstart an S/4 implementation? And what we decided to do is team up with AWS and leveraging machine learning, artificial intelligence, most importantly, standing up an environment on AWS. We actually created what we call Kinetic Finance Startup. Many of our clients are choosing to start with finance and specifically SAP central finance to begin their journey to the new S/4HANA environment. And what we've been able to do is create a touchless build solution, so over a weekend, we can actually connect to your existing ERP solution. Majority of those is starting with an ECC environment. We can extract the data, we can use harmonization rules to actually change and modify your data and optimize it for the future. And then we actually through completely touchless built-in automation, stand up a brand new AWS environment with S/4HANA on it and actually automate the configuration and testing of the basic financial transactions. So when you come in the next week and we start the conversation with the client, we're actually looking at a real life S/4HANA system on AWS with their mas... >> Oh, that's... >> So the whole concept is to change how we engage. >> Right. So again, I don't know that I were to think of finance as kind of a lead application, to start this journey. I mean, I can see on one hand, it is the system of record and it, you know, it has a lot of very important information that's got to eventually get into finance. On the other hand, it seems like there's less critical, maybe lower hanging fruit that's less risky. Is it because you can run it kind of in a parallel path for some period of time, but it strikes me that finance might not be the first place you go to look for some early wins. >> It's actually what you just said about the parallelism. So the reason we've seen that finance actually was one of the starting points is even if you look at the history of SAP's S/4HANA solution, way back before we got to that, it started with a concept called smart accounting or simple finance. And the theory here is, you could actually... If a company has, let's say multiple ERPs, as most do, you can actually grab the financial information, bring it into a new S/4 or central finance environment, and actually combine or merge the accounting information to get improved reporting, optimize a shared service organization. So it's actually a lower risk way to start the journey before going and touching the heart of the business or core operations, or manufacturing, for example, >> That's pretty interesting. So you run it in parallel for a while and then eventually does, is the plan that it takes over, from the old. So it is effectively kind of, I guess, a slightly delayed lift and shift, or maybe it's a reassemble and then a flip. I don't know how you would describe it because it's not really lift and shift. >> It's not really lift and shift actually, you have two options. You can either over time pull all of your business processes out of the underlying ERP solutions and bring them into the S/4HANA environment or multiple S/4HANA environments. Or some companies may choose to continue to... (Kelly hangs) Especially if you're in an industry where you do a lot of acquisitions or divestitures, you may not have an intention of ever combining all of your ERPs, but you may want to change each of them to S/4HANA underneath, and then have one environment in which you're pulling your data together to really consolidate your financial reporting. >> That's great. I want to follow up on something that you mentioned, which is the use of machine learning and artificial intelligence. And we talk a lot about, right? Those are hot buzzwords all over the place, but, you know, I'm pretty vehement in that, you know, general purpose AI and ML is kind of interesting, but where the real interesting stuff ends is where the rubber hits the road, is in applied. And it sounds like you've got a pretty interesting application where you're applying this technology to help make this move to cloud go a little bit smoother. >> Yes. One of the areas, you know, since we've been talking a bit about finance then I'll use it as an example. Is if you think about it, whenever we go in and we're typically working with... (Kelly hangs) especially in finance, you know, one of the topics is, how to optimize a chart of accounts? So over time we've done this hundreds of times, if we can look at different sectors, different industries, we can use benchmark chart of accounts. So instead of making this a paper-based exercise that individuals are doing, why not take that and actually use artificial intelligence machine learning to create data harmonization rules, so that technologies can actually do that same work. And so that's been one of the things we've been working on that I personally find very interesting just in my finance background. >> Right. And is this a relatively new thing, or have you guys been doing this for a while? >> Actually, it's something that over the last 12 months, we've been focused on building out in partnership with AWS. So it's fairly new. >> That's great. I want... I'd love to shift gears a little bit, and talk about COVID, and the impact of COVID on your business. Clearly in March, right? It was the light switch moment and everybody had to work from home and it was a quick rush to make sure that everybody was safe and we could support our remote workers, that said, can't help with the ba... All the bad stuff that's happening in hospitality and travel, and a whole lot of other industries. So that aside and that's bad stuff. In the tech industry, we were able to make the move, but now we know we're six, seven, eight months into this thing, and it's clear that, you know, we're going to have many elements of this going forward for a while. So I'm curious just from your business and your customer point of view, if you can share, you know, kind of the contrast of what happened in March and April to what you're seeing now and how this new reality, whatever this new reality is going to be, as we, you know, continue to evolve is impacting this digital transformation conversations? >> It is interesting. So if I pivot back to March, when this all occurred, you know, it truly did feel an instant going from in-person. And as consultants we travel and typically have a Monday through Thursday, or Monday through Friday type of travel schedule to an instant working from home overnight. And, you know, I'm really proud of our teams and how they seamlessly made that transition. Many, including myself, were actually leading clients through final cut overs in parallel to this happening. And we were able to really pivot and make those shifts, and I was reflecting with one of the executives I worked with, you know, she and I, you know, six months later, we're looking back at how we did that and how impressed we were with what the team pulled off. And since then, they've been able to do several other go lives, which is great. But I think that it was something we had to do quickly. I think many would have said it couldn't have been done that you would see the whole world move to a working from home environment, but we did. What it tells me is it gives me a lot of hope for a lot of the things that businesses can do in the future. In the past we used to constrain ourselves of, Oh, there's no way we could ever get XYZ done, or we can't make this type of change in the world, but we can. If I flash forward to now, I think we're very settled in kind of this new way of working, but I'm also hopeful for what the future is going to look like. I don't believe it will be a pivot all the way back to... Especially for consultants traveling on a regular basis of Monday through Friday. Instead, I think we're going to create models that give people and organizations the flexibility they need to really balance some of their personal responsibilities along with their work responsibilities. My hope and expectations is that also opens up options so that all organizations have access to more talent that they may not have had before. And I think that also means global talent. I think we're showing we can work as global teams, which means, you know, I could now have members from Japan joining, you know, my permanent leadership team in ways that I maybe never have thought of before. Those are just some examples of what I expect and hope for all of us that we'll see coming out of this. >> Hopefully and I know... Like you said, you've been a consultant for years and years and years, and you guys spend lots of time on airplanes, and hopefully you don't have to spend quite so much time on airplanes because you don't necessarily have to be there all the time. But you talked about an interesting thing and that's talent and opening up the opportunity to get more talent that maybe you wouldn't have ever considered. And along those same lines, right? Is the move in diversity and inclusion. And I just watched a show that you did a few months ago, called the... "A Chance for Change: Accelerating Business Recovery, Through Gender Diversity," on a Facebook interview, very cool panel, really enjoyed it. And I want to follow up on some of those things, 'cause you've made some really simple and poignant points. And one of the things that you said definitively, go back to the wide diverse talent and perspective equals winning in business, period. I love that. You know, we hear this all the time that, you know, not only is it the right thing to do, but it's also good for business. And isn't it nice when those two things can actually line up. And you just talked about, you know, in more of a generic sense, the ability to open up your talent window when there's a worldwide talent shortage, both for geography, but also the work in diversity and inclusion and to continue to hold the momentum that continues to build in this area. I wonder if you could, you know, kind of share your thoughts on that, and your position and what's going on with Deloitte. >> Absolutely. You know, I do think this is one of those key pivotal moments for all of us, and I believe we have, coming out of this an option to really move the needle on our diversity and inclusion, and equality efforts. You know, one example I think about women, women in leadership positions. You know, being in consulting, you know, one of the challenges has always been that we do travel a lot, and it can be difficult to balance all the responsibilities, professional and personally. I think with a move to more flexible work arrangements, less travel, or travel for purpose is what I would highlight for the future. I think it opens the door to many more women being able to have careers in consulting, if that's what they, you know, had desired. I also think it allows them to have... You know, spend their entire careers in consulting and in ways we never saw before. And that means you'll see as significant movement and women in leadership positions. I also think this applies to underrepresented minorities. I hope that from all of this, instead of there may be companies that focus on recruiting from, you know, schools that are local to them or within their surrounding areas. I think this gives us an opportunity to really open that aperture up and look at talent from any school or university, or geography, and being able to get the right skill sets in the door and the right talent. Therefore you can actually see movement and diversity within teams, as well as at the leadership levels for URMs. >> Right. Right. And really managing to the right things too. I think that's the other thing that's coming out of this, and we've had a lot of conversations on work from home or work from anywhere. You guys are a little bit different than the consultant 'cause your team is there, usually local at the client site for some period of time. But for a lot of people, it's the first time they are not sitting across from a desk or, you know, within close proximity. Now you too, in your teams. And so, the shift changes that now you have to judge output, (Jeff chuckles) and not activity. And you would think that that would be a great and easy thing to execute, but we're hearing more and more that it's not necessarily. And you really highlighted, I think, three leadership traits that are always important, but more important now than ever before in that other interview. And I just want to call them out 'cause I thought it was worth calling out. You know, empathy has never been more important. Resilience, and my favorite one you said at the end, calm in the storm. I just wonder again, if you could share, you know, kind of, as you've gone through it, both, you know, as somebody at Deloitte within the greater Deloitte group, but then also in managing your own teams, to maintain that calm in the storm and to maintain, you know, empathetic leadership, because I think you've said it before, right? This is a personal challenge that we're all going through. We all have different things going on at home, whether it's the spouses working, the kids are doing homeschool. People are taking care of older parents, this and that. It's a real personal thing, and so these leadership characteristics, these softer leadership characteristics have never been more important >> That's so true. And, you know, when I think about the empathy part, right now what we're going through is also about how is each of us as leaders also sharing a bit more about how we're experiencing this? I think the sharing of stories is what also helps many on the teams adapt, adjust. The reality is when you're working on camera all day and, you know, in the past, imagine that you maybe were having a tough day or you weren't feeling that great, you weren't on camera all day with every one of your coworkers. You we're actually, you know, sitting in an office, you may have to go to the conference room to do some meetings, but you didn't look... (Kelly hangs) like someone was kind of staring at you all day long. Now, when we're working from home virtually and we're on Zoom or Skype or WebEx, et cetera, all day, it does feel like you're under the lights when you're on camera. And there's a lot of pressure and people are trying to figure out how to manage their own emotions while doing that. And, you know, my message would say as an empathetic leader, it's okay for you to also share when you might be having a tough go that day. Maybe one of your children has been kind of acting out and they didn't really want to do the virtual school. It's okay to share in that because everyone's going through it, and it makes us all more human. >> Right. >> And it makes us all more connected. >> Right. Well, I will share with you a pro tip, we've done a few of these interviews and it is okay to let people turn off the camera. And I think as a manager, I think it's actually an okay thing to say, okay, everyone, let's just turn off our cameras and get a break from that camera that's got that eye on you all the time, because it is just another, you know, kind of a factor that we have to deal with. Well, go ahead. >> And I was going to ask, what do you actually, you know, I don't know what one of your techniques is, but I know mine is some of the meetings, it's actually just go back to traditional telephone calls (Jeff chuckles) and actually even just being on your cell, put on your air, you know, your earbuds, or your headphones and even walk. >> Right. >> So I think the other thing we're all missing is actually that movement, the steps to go to the coffee maker and back, or to lunch and back, we don't have them anymore. So you've got to work extra hard, actually getting those extra steps in calories and just mental breaks at times. >> Yeah, well then there's a whole another tranche on walking during meetings. And I used have a boss that I would only do one-on-ones while we took a walk. He always says, I get in there... 'Cause then there's, you know, you're not necessarily looking at each other. And if there's some sensitive things or tough conversations, sometimes it's easier if you're not just looking across the table at one another with all the silence. So there's a lot to be said for that as well. Well, Kelly, I really enjoyed this conversation and getting to meet you for the first time. It sounds like you're doing a lot of cool and exciting things and, you know, exciting speed and innovation with SAP, that's noble work and I'm sure a lot of people are really happy to have you help them out there. So thank you very much for your time and to have a great AWS re:Invent. >> Thanks, Jeff. It was great to discuss this with you. >> Absolutely. All right. She's Kelly, I'm Jeff. You're watching theCUBE's ongoing coverage of AWS re:Invent 2020. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of Dell Technologies world, digital experience, brought to you by Dell Technologies. >> Hi, I'm Stu Miniman and this is theCUBE coverage of Dell Technologies world digital experience. Happy to welcome to the program. First we have a first time guest Akanksha Mehrotra, she's the Vice President of Marketing with Dell Technologies. Joining us one of our CUBE alumni, Caitlin Gordon, she's the Vice President of Product Marketing, also with Dell Technologies. Caitlin, welcome back, Akanksha welcome to the program. >> Thank you Stu, happy to be here. >> Alright, so one of the big models we've been talking about for the last few years is a change in how customers acquire things, big thing we've talked about, for many years, this shift from CAPEX to OPEX. How cloud is impacting everything Jeff Clarke in the keynote was talking about, it's the Dell Technologies on demand, DTOD, I guess is the, four letter acronym we use Akansha help us understand a little bit from your standpoint, what is it? Why is it important to your customers? >> Yeah, so Stu, as soon as you as you heard, as part of the keynote, from from Jeff and others earlier today, we've been working really hard to bring the benefits of on demand IT to our customers, in private cloud, public cloud and edge. And certainly this year, especially, we've seen a lot of interest in this, COVID have catalyzed customer interest in flexible consumption in as a service. As we talk with our customers and partners, we hear this almost daily, it's required a level of agility that candidly traditional CAPEX based models simply haven't been able to provide, I mean, imagine taking your workforce remote over the weekend, and the stress that puts on your infrastructure. And so I think that's kind of forced IT to consider some of these alternatives. Another factor has also been, companies have been wanting to preserve capital, right, and avoid large cash outlays and having this type of flexibility and being able to pay for infrastructure, as you're using it, it gives them a way to do that. So I mean, those are some of the customer drivers that we've seen. Last year at Dell Tech Summit, around the this time last year, actually, in November timeframe, we introduced Dell Technologies on demand as our umbrella program for a flexible consumption and as a service solutions. And really what it what it seeks to do is make it easier for customers to get the simplicity and flexibility of cloud, along with the performance and security of on-premises infrastructure. So it's giving them a range of consumption models that include both payment option as well as services that they can apply on any one of the products in our portfolio from end user devices to core data center infrastructure to hybrid cloud solutions. And we've announced that last year, one of the things that you heard about today, and that we're announcing over this event is that we're continually looking to make it easier and simpler for our customers with various turnkey offerings and simpler offerings for them, given the interest that we've seen. >> Yeah, I want to key off of, you mentioned the impact of COVID-19. And for your customers, it's something we've definitely seen that the promise of cloud always has been to be highly flexible, we can scale up, we can scale down. We know that some services out there aren't always as flexible as we might hope. There's certain SaaS solutions, where you're signing up for a multi year offering and even for the cloud, I might lock in some savings by buying something in bulk. So help us understand, what are the benefits that your customer sees, the savings that they get and is this truly cloud flexible, which means I can burst up and scale as I need. And I can it reached the point, oh, hey, I need half the capacity for the next six months. Can I do that? >> Yeah, absolutely. So, Stu we actually commissioned IBC to talk to a few of our customers. So let me maybe share some of the benefits that they saw in broad terms, and then I can maybe share a specific example of what a particular customer saw. So we had IDC talk to several of the customers using Dell Technologies on demand models, various GIOS, and various sort of sizes. And what they found was that on average, they saw about a 23%, lower cost of storage operations per year, which is great, right? Lower cost of operations is always great. IT is always looking for those efficiencies, especially, in the current environment, but that's not all. I think that's just sort of part of the story. What they also shared with us is that, these types of models were able to help them become much more agile in how they work and change how they work. And what they found was that they saw 54% fewer incidents of downtime and they were 92% faster in their ability to deploy storage capacity, because they had that capacity in their data center available ready for that spike when their business saw it. ` So those are just some of the broad examples of what our customers have seen. Another specific example that I would would share with you is a large multinational institution, financial services company, we've been working with them for years to service their, enterprise scale, private cloud. And then more recently, they had us also, manage their storage as a service managed utility. And they've seen phenomenal results, they've been able to get 50% more compute power at 8%, lower cost, and 90% faster or reduce time and provisioning data. It's all about the yes, it's about the cost savings but really, it's about the agility that the business gets, right. And as you started out, right, with COVID, they really needed that agility and that flexibility and having these models available, ready to spike, ready to go down, right, have been able to provide that. >> Yeah, I think another thing we've seen is, people rush to cloud because it promised that agility, and we've had those conversations before is, there's a reality of what that means, which it might not be the resiliency you're looking for, it also might not actually be as simple as he thought it might be. And we're seeing some of that come back on-prem, whether you need resiliency or performance or security, or you don't want to be really locked into a specific public cloud but you still want to have that agility in the benefits of really running your data center in a service oriented model. And that trend has been picking up over the past couple years. And as we've already said a couple times today, we've seen that accelerate, but also, we starting to see more customers ask for it. It's not just the big and more strategic and the aggressive customers that are looking for this more and more customers are kind of seeing that this is the end game and that's kind of leads into where we're going, which is, how do we make this more accessible to others? >> Well, Caitlin, you're using one of one of my punch lines that I've used for a number of years now if remember, when we thought that cloud was inexpensive and easy to use, it's not. And if we look at what customers are doing, it's a hybrid model. They're deploying in multiple environments, we're seeing the public cloud look more like the enterprise, the enterprise look more like, the public cloud. So these offerings have, OPEX flexibility and the like, make a whole lot of sense. So you've said that, you've seen a lot of growth, especially this year, any metrics you can give us on, adoption, love the one customer example, in the financial space, anything else to kind of paint the picture as to, how prevalent this is becoming. >> Yeah, maybe I'll get started. So, we've seen nearly 50%, year over year growth in the customer base or our most recent quarter, and it's growing, we've seen over 500% increase, year on year in signed contracts, customer demand in these types of models has caused us to expand our offerings to into countries like Brazil, Chile, Colombia, India, and China. I mean, we already offered about 50 plus countries and along with our partner, network and even more, so, I mean, those are just some of the data points around business traction. In the models that we have another proof point that I could point you to is that, in April, we include, we announced a payment flexibility program, which gave our customers a number of promotions and options to extend this flexibility into, across our portfolio and into other parts of our businesses. And just recently, about a month ago, we extended that, and we've seen really good traction in that as well. So I think overall, like you said there's aspects about public cloud that customers really like, and they tell us, hey, I want to be able to pay as I go, I want to be able to extend and contract the infrastructure as I'm using it. I want a simple management experience. But then as Caitlin said, they realize that Oh, but I don't want to, pay for the refactoring and then the egress and the ingress charges and some of my workloads are better off on premises for performance, locality, security, compliance reasons, right. And therein lies the promise of as a service for on-prem infrastructure, 'cause really, I keep looking for the best of both worlds. And this gives you that right you can use the consumption models to grow and shrink as you needed, you can us the payment models to only pay for what you're using and along with our partner network, you can have in the location that you want so you can sort of have your cake and eat it too. >> Yeah, and I would just add on to that is that more and more of the conversation is both about how can I consume that more as a service and pay for just what I'm using? But also, how can I spend less time maybe zero time and energy actually managing that infrastructure? And how can I then allocate the time energy resources into running my business and investing in more strategic things? So becomes both an important financial conversation but even more so a conversation about how IT can empower the business, which really just changes what we're able to do for customers. So it's an exciting kind of transition to see this really evolve into really not talking about products anymore, and helping our customers have all their business. >> Well, Caitlin, that's a really interesting point, I want you to talk to us a little bit about the Dell Tech storage as a service, how does that fit, we were just talking about don't want to talk about products, we want to talk about really moving to that full OPEX model so help connect the dots for us. >> Yeah, so we're really excited about this, this will be coming in the first half of next year, as you probably heard earlier today. And what we're doing here is we've really taken what we already have had in market. And we've really upped that to the next level, we've accelerated the simplicity of what we offer here. And think of the experience is all starting in a single console, where you just pick up four things, what's the type of storage you want, what's the performance you want, how much and for how long, that's it. And then now we're counting the time from then to when it's in your data center in days, not months, not weeks, but in days and we're able to get you up and going. And it's your data center of your choice, whether that's on-prem in your own data center, or at a colo facility, we bring that equipment in, we get that deployed, we manage it for you, you operate it, and you simply pay for what you use. So you're really in a quick time to value you're in a very simple model and you're not really responsible for managing infrastructure that's really on us. And that moves you into being in a true OPEX model and it also enables you to accelerate what you're able to leverage that whether it's Blob Storage, file storage, you can get up and running quickly and let us worry about how to manage the infrastructure and we give you the ability to operate what you need to. >> Caitlin, maybe if you could give us a little bit of color as to what happens behind the scenes to make that work. As it sounds wonderful, you've had the program around for a year, these aren't trivial things that you're talking about all the logistics, the management the the gear, and making sure that the physical and the power and everything is all set. So help us understand the engineering, the development, and what this means from kind of a services and go to market that make a solution like this work. >> Yeah, and a lot of ways we're having to change our entire business to help our customers change there's, it goes from top to bottom, and you'll get to hear a lot more about it when we're actually available next year. But when you think about it, we have a lot of the DNA, we have a lot of the experience, we have the technology, but we almost have to completely flip the script on ourselves of how we deliver it, who our customer is, what our then end user customer needs from us, and what the role of things like our global services organization is what the role of our global sales organization is and how do we accelerate providing outcomes to our customers and get the rest out of their way. And the fact that I haven't mentioned a product name, but by the way, we actually have industry leading products and pretty much every category. So of course, on the back end, all of this is going to be powered by our industry leading storage solutions, like power store will be in your data center but at the same time, we will actually have worked to really masked that you don't even need to know that nor do you need to really operate much beyond what you need to really run your business. And that's really it's been an interesting work for us to just flip how we think about everything and you'll hear a whole lot more about it next year as we really bring this out into market but it's been really fun and a big learning for everyone. >> Excellent well yeah, something something power is underneath there well Caitlin. All right why don't you both give us the final takeaway for the Dell Tech on demand account. Start with you in just give us the final takeaway. >> Yeah, so I think look, I back to kind of what we were talking about, we've actually been offering these types of solutions to our customers for a really long time. Through Dell financial services, we've been offering payment flexibility for over 23 years, over 15 years and manage utility. So the customer example that I gave you is a customer who's running storage as a service and has been for many years, I think, building on that experience, listening to our customers feedback over that time period and over, of course, this past year, we're looking to apply all of that, to make it even more simpler for them to consume our infrastructure in the near future. And so, storage as a service is going to be a really exciting proof point of that, the momentum stats and some of the other things that I shared with you today and that you're going to hear about over the next couple of days or another proof point of it. But we're excited about this, and looking forward to continuing the dialogue with our customers with our partners and (mumbles) >> Then I would I'll kind of play off of one of your words there which is is all about simplicity for us is how do we take what we've been able to do for a lot of our customers accelerate that and simplify it to a point where we can offer that for all of our customers. And we're really looking to accelerate this first with storage and then get all of our offerings really into this model, because it's really about getting our customers out of managing infrastructure and give them the time, energy, resources to manage their business and simplicity is paramount to making sure that happens. >> Caitlin and Akanksha, thank you so much for giving us the updates. Congratulations to all the progress and definitely looking forward to hearing more beginning of next year. Thanks for joining. >> Thank you Stu. >> Thank you Stu >> All right, I'm Stu Miniman this is Dell Technology world digital experience. I'm Stu Miniman. And thank you as always for watching theCUBE (upbeat music)

>>from around the globe. It's the Cube covering Google Cloud. Next on Air 20. Hi, I'm Stew Minimum And and this is the Cube's coverage of Google Cloud next 20 on air, Of course. Last year we were all in person in San Francisco. This year it's an online experience. It's actually spanning many weeks and this week when we're releasing the Cube interviews, talking about application modernization, happy to welcome back program two of our Cube alumni. Chris Well, I've got Aparna Sinha, Uh, who is the director of product management, and joining her is Pali Bhat, who's the vice president of product and design, both with Google Cloud Poly. Welcome back. Thanks so much for joining us. >>Thank you. Good to be here. >>Well, so it goes without saying it. That 2020 has had quite a lot of changes. Really affect it. Start with you. You know, obviously there's been a lot of discussion is what is the impact of the global pandemic? The ripple in the economy on cloud. So I would love to hear a little bit. You know what you're hearing from your customers. What? That impact has been on on you and your business. >>Yes to thank thank you for asking as I look at our customers, what's been most inspiring for me to see is how organizations and the people in those organizations are coming together to help each other during this unprecedented event. And one of the things I wanted to highlight is, as we all adjust to this sort of new normal, there are two things that I keep seeing across every one of our customers. Better operation efficiency, with the focus on cost saving is something that's a business imperative and has drawn urgency. And the second bit is an increased focus on agility and business innovation. In the current atmosphere, where digital has truly become gone from being one of the channels being D channel, we're seeing our customers respond by being more innovative and reaching their customers in the way that they want to be rich. And that's been, for me personally, very inspiring to see. And we turned on Google Cloud to be a part of helping our customers in this journey in terms of our business itself. We're seeing tremendous momentum around our organization business because it plays directly into these two business imperatives around operational efficiency, cost saving and, of course, business innovation and agility. In Q two of 2020 we saw more than 100,000 companies use our application modernization platform across G ke and those cloud functions Cloud Run and our developers tools. So we've been, uh, just tagged with the response of how customers are using our tools in order to help them run their businesses, operate more efficiently and be more innovative on behalf of their customers. So we're seeing customers use everything from building mission critical applications who then securing, migrating and then operating our services. And we've also seen that customers get tremendous benefits. We've seen up to a 35% increase simply by using our own migration tools. And we've also seen it up to 75% improvement to all of the automation and re platform ing that they can do with our monetization platform. That's been incredible. What I do want to do. Those have a partner chime in on some of the complexity that these customers are seeing and how we're going about trying to address that >>Yes, eso to help our customers with the application modernization journey. Google Cloud really offers three highly differentiated capabilities. Us to the first one is really providing a consistent development and operations experience, and this is really important because you want the same experience, regardless of whether you're running natively in Google Cloud or you're running across clouds or you're running hybrid or you're running at the edge. And I think this is a truly unique differentiator off what we offer. Secondly, we really give customers and their developers industry leading guidance. And this is particularly important because there's a set of best practices on how you do development, how you run these applications, how you operate them in production for high reliability, a exceptional security staff, the stature and for the maximum developer efficiency on. And we provide the platform and the tooling to do that so that it can be customized to it's specific customers needs and their specific place on that modernization journey. And then the third thing on and I think this is incredibly important as well is that we would ride a data driven approach, a data driven optimization and benchmarking approach so that we can tell you where you are with regard to best practice and then help you move towards best practice, no matter where you're starting. >>Yeah, well, thank you, Aparna and Polly definitely resonates with what we're hearing. You know, customers need to be data driven. And then there's the imperative Now that digital movement Pali last year at the show, of course, Antos was, you know, really the talk of the conference years gone by. We know things move really fast, so if you could, you know, probably don't have time to get all of the news, but share with us the updates what differentiated this year along from a new standpoint, >>Yeah, So we've got tremendous set off improvements to the platform. And one of the things that I wanted to just share was that our customers as they actually migrate on to onto the cloud and begin the modernization journeys in their digital transformation programs. What we're seeing over and over is those customers that start with the platform as opposed to an individual application, are set up for success in the future. The platform, of course, is an tos where your application modernization journey begins. In terms of updates, we're gonna share a series off updates in block post, etcetera. I just want to highlight a few. We're sharing their availability off Antos for their middle swathe things that our customers have been asking about. And now our customers get to run on those on Prem and at the edge without the need for a hyper visor. What this does is helps organizations minimize unnecessary overhead and ultimately unlock all of the new cloud and edge use case. The second bit is we're not in the GF our speech to text on prem capability, but this is our first hybrid AI capability. So customers like Iron Mountain get to use hybrid AI, so they have full control of the infrastructure and have control off their data so they can implement data residency and compliance while still leveraging all of Google Cloud AI capabilities. Third services identity again. This extends existing identity solutions so that you can seamlessly work on and those workloads again. This is going to be generally available for on premise customers and better for Antos on AWS, and you're going to see more and more customers be able to leverage their existing identity investments while still getting the consistency that Anton's provides across environments. In the last one that I like to highlight is on those attached clusters, which lets customers bring any kubernetes conforming cluster on Toronto's and still take advantage of the advanced capabilities that until provides like declarative configurations and service automation. So one of the customers I just want to call out is Cold just built it. Entire hybrid cloud strategy on Anton's Day began with the platform first, and now we're seeing a record number of customers on Cold Start camaraderie. Take advantage of Mantel's tempting. With Macquarie Bank played, there's a number of use cases. I am particularly excited about major league baseball. I'm a big fan of baseball, and Major League Baseball is now using and those for 2020 season and all of the stadium across, trusting a large amount of data and gives them the capability to get those capabilities in stadiums very, really acceptable. All of those >>Okay, quick, quick. Follow up on that and those attached clusters because it was one of the questions I had last year. Google Cloud has partnerships with VM Ware for what they're doing. You know, Red Hat and Pivotal also is part of the VM Ware families, and they have their own kubernetes offering. So should I be thinking of this as a management capability that's similar to like what? What Andrew does Or maybe as your arca, Or is it just a kind of interoperability piece? How do we understand how these multiple kubernetes fit together? >>Yeah. So what we've done with Antos has really taken the approach that we need to help our customers are made and manage the infrastructure to specifically what Antos attach clusters gives our customers is they can have any kubernetes cluster as long as it's kubernetes conformance, they can benefit from all of the things that we provide in terms of automation. One of the challenges, of course, is you know, those two is configuring these very, very large instances in walls. A lot of handcrafting today we can provide declarative configuration. So you automate all of that. So think of this as configures code I think of this is infrastructure scored management scored. We're providing that service automation layer on top of any kubernetes conforming cluster with an tools. >>Great. Alright, uh, it's at modernization weeks, so Ah, partner, maybe bring us in aside. You were talking about your customers and what their what they're doing to modernize what's new that they should be aware of this year. >>Yeah, so So, First of all, you know, our mission is really to accelerate innovation in every organization through making their developers more productive as well as automating their operations. And this is something that is resonating even more in these times. Specifically, I think the biggest news that we have is really around, how we're going to help companies get started with the application modernization so that they can maximize the impact of their modernization efforts. And to do this, we're introducing what we're calling. The Google Cloud Application Modernization program or a Google camp for short on Google Camp has three pieces. It has an assessment, which is really data driven and fact based. It's a baseline assessment that helps organizations understand where they are in terms of their maturity with application modernization. Secondly, we give them a blueprint. This is something that is, is it encapsulates a specific set of best practices, proven best practices from development to security to operations, and it's something that they can put into practice and implement immediately. These practices, they cover the entire application lifecycle from writing the code to the See I CD to running it and operating it for maximum reliability and security. And then the third aspect, of course, is the application platform. And this is a modern platform, but also extremely extensible. And, as you know, it spans across clouds on this enables organizations to build, run and secure and, of course, manage both legacy as well as new applications. And the good news, of course, here is you know, this is a time tested platform. It's something that we use internally as well. For our Cloud ML services are being query omni service capability as well as for apogee, hot hybrid and many more at over time. So with the Google campus really covered all aspects of the application lifecycle. And we think it's extremely important for enterprises to have this capability. >>Yeah, so a party when you talk about the extent ability, I would expect that Google Cloud Run is one of the options there to help give us a bridge to get to server list. If that's where customers looking to my right on >>that, that's rights to the camp program provides is holistic, and it brings together many of our capabilities. So Cloud Code Cloud See I CD Cloud Run, which is our server less offering and also includes G ki e and and those best practices. Because customers for their applications, they're usually using multiple platforms. Now, in the case of Cloud Run, in particular, I want to highlight that there's been a lot of interest in the serverless capability during this last few months. In particular, I think, disproportionate amount of interest and server lists on container Native. In fact, according to the CNC F 2020 State of Cloud Native Development Report, you might have seen that, you know, they noted that 2.7 million cloud native developers are using kubernetes and four million are using serverless architectures or cloud functions, and that about 60% of back and developers are now using containers. So this just points to the the usage that was happening already and is now really disproportionately accelerated. In our case, you know, we've we've worked with several customers at the New York State Department and Media Market. Saturn are two that are really excellent stories with the New York State Department. They had a unemployment claims crisis. There was a lot. Ah, volume. That was difficult for their application to handle. And so we worked with them to re architect their application as a set of micro services on Google Cloud on our public sector team of teamed up with them to roll out a new unemployment website in record time. That website was able to handle the 1600% increase in Web traffic compared to a typical week. And this is very much do, too, the dev ops tooling that we provided and we worked with them on and then with Media market Saturn. This is really an excellent example in EMEA based example of a retailer that was able to achieve an eight X increase in speed as well as a 40% cost reduction. And these are really important metrics in these times in particular because for a retailer in the Cove in 19 crisis, to be able to bring new applications and new features to the hands of their customers is ultimately something that impacts their business is extremely valuable. >>Yeah, you think you bring up a really great point of partner when I traditionally think of application modernization. Maybe I've been in the space to long. But it is. Simplicity is not. The first thing that comes to mind is probably pointed out right now. There's an imperative people need to move fast, so I want to throw it out to both of you. How is Google's trying to make sure that, you know, in these uncertain times that customers can move fast and that with all these technology options that it could be just a little bit simpler? >>Yeah, I think I just, uh you know, start off by saying the first thing we've done is build all of our services from the ground up with automation, simplicity and agility in mind. So we've designed for development teams and operations teams be able to take these solutions and get productive with them right away. In addition, we understand that some of our largest customers actually need dedicated program where they can actually assess where they are and then map out a plan for incremental improvement so they can get on their journey to application modernization. But do it with the highest our way. And that was Google camp that apartment talked about ultimately at Google Cloud. Our mission, of course, is to accelerate innovation. Every organization toe hold developer velocity improvements, but also giving them the operation automation that we talked about with that application modernization platform. So we're very excited to be able to do this with every organization. >>Great. Well, Aparna, I'll let you have the final word Is the application modernization week here at Google Cloud. Next online, you can have the final take away for customers. >>Well, thank you, cio. You know, we are extremely passionate about developers on. We want to make sure that it is easy for anyone, anywhere to be able to get started with development as well as to have a path to, uh, accelerated path to production for their applications. So some of what we've done in terms of simplicity, which, as you said is extremely important in this environment, is to really make it easy to get started on. Some of the announcements are around build packs and the integration of cloud code are plug ins to the development environment directly into our serverless environment. And that's the type of thing that gets me excited. And I think I'm very passionate about that because it's something that applies to everyone. Uh, you know, regardless of where they are or what type of person they are, they can get started with development. And that can be a path to economic renewal and growth not just for companies, but for individuals. And that's a mission that we're extremely passionate about. Google Cloud >>Apartment Poly Thank you so much for sharing all the updates. Congratulations to the team. And definitely great to hear about how you're helping customers in these challenging times. >>Thank you for having us on. >>Thank you. So great to see you again. >>Alright. Stay tuned for more coverage from stew minimum and, as always, Thank you for watching the Cube. Yeah, yeah.

>> live from Boston, Massachusetts. It's the queue covering your red hat. Some twenty nineteen lots. You buy bread >> and good morning. Welcome to Beantown, Boston, Massachusetts to Mina Mons Hometown by the police Town of residents. John Wallis was stupid from here on the Q. Bert had summit and stew for you. Good to see you here. And a home game. >> Yeah, John, Thanks so much. Nice. You know, Boston, The Cube loves Boston. The B C E C is actually where the first cube event was way back in twenty ten. And we wish there were more conferences here in Boston. Gorgeous weather here in the spring. Ah, little chilly at night with the wind coming off the water, but really good. Here is the sixth year we've had the Cube here, right? Had some in my fifth year at the show. Great energy. And, you know, thirty four billion reasons why people are spending a lot of time keeping a close eye on. Let's just know. Yeah, >> jump right in thirty four billion dollar deal. I am red hatt gotta prove by doj uh, here in the States. But there's still some hurdles that they have to get over in order for that to come to fruition, Maybe later this year. That's the expectation. But just your thoughts right now about about that synergy about that opportunity that that we think is about to have. >> Yeah, so? So right, let's get this piece out of the way. Because here at the conference, we're talking about Red Hat. The acquisition has not completed. So while the CEO of IBM you know Jenny will be up on stage tonight along with, you know, Jim White Hirsi over at Hat and Sakina della, you know, flying in from Seattle, where you might get your name yesterday. So you know, at least two of those three your Cuba Lem's. So we'LL get Jenny on one of these days. But, you know, this is a big acquisition, the largest software acquisition ever, and third largest acquisition in tech history. Now we watched the first biggest tech acquisition in history, which was Del buying AMC just a couple of years ago. And this is not the normal. Okay? Hey, we announced it and you know, it closed quietly in a few months. So as you mentioned, DOJ approved it. There's a few more government agencies Europe needs to go through. You never know what China might ask to come in here, but, you know, really, at the core if you look at it, you know, IBM and Red Hat have worked together for decades. You know, we wrote a lot about this when the announcement happened. You know, IBM is no stranger to open source. IBM is no stranger to the clinics and the areas where Red Hat has been growing and expanded too. You see, IBM, they're so communities, you know, super hot space. If you look, you know, Red hat is they're they're open shift platform, which is what Red Hat does for cloud. Native Development has over a thousand customers. They're adding between one hundred one hundred fifty a quarter is what they talk about publicly. We're gonna have some of those customers on this week. So huge area. That multi cloud hybrid cloud world absolutely is where it's at. We did four days of broadcast from IBM. Think earlier this year in San Francisco. And, you know, once again, Jim white hairs and Jenny were on stage together. They're talking about where they've been working together for a long time. and just, you know, some things will change, but from IBM standpoint, they said, Look, you know, the day after this closes, you know, Red Hat doesn't go away. That had just announced new branding, and everybody's like, Well, why are they changing their branding? You know, when you know IBM is taking over and the answer was, Look, Red Hat's going to stay as a standalone entity. IBM says they're not going to have a single lay off, not even HR consolidation, at least in the beginning. We understand, you know, give me your stuff to work out some of these pieces, but there are ears. They will work together. I look at it. John is like the core. What is the biggest piece of IBM's business is services. That Army of services, both from IBM and all of their Esai partners and everybody they worked with Khun really supercharge and help scale some of the environment that red hats doing so really interesting. Expect them to talk a little bit about it. Red hat is way more transparent than your average company. They had an analyst event like a week or two after it happened, and I was really surprised how much they would tell us and that we could talk about publicly. As I said, just cause I've seen so many acquisitions happen, including some you know, mega ones in the past. And we know how little usually you talk about until it it's done and it's signed. And, you know, the bankers and lawyers have been paid all their fees. >> Let me ask you, you raise an interesting point. Um, you know that there are some different approaches, obviously, between IBM redhead, just in terms of their institutional legacies in terms of processes. Red hat. You mentioned very transparent organization. Open source. Right. So we're all about the rebrand. They come out, you know, the drop shadow, man, They got the hat. What's that cultural mix going to be like? Can they truly run independently? Yeah, they're a big piece. So And if your IBM can you let that run on its own? >> So, John, that is the question most of us have. So, you know, I've worked with Red Hat for coming up on twenty years now, you know, Remember when Lennox was just this mess of colonel dot organ. So much changes that red hat came and gave, you know, adult supervision to help move that forward on. The thing I I wrote about is what Red Hat is really, really good at. If you look at the core, there do is managing that chaos and change on the industry. If you look how many changes happen, toe Lennox, you know every you know, day, week, month and they package all that together and they test all that same thing in Kou Burnett is the same thing in so many different spaces where that open source world is just frenetic and changing. So they're really geared for today's industry. You talk what's the only constant in our industry? John is it is changed. IBM, on the other hand, is like, you know, over one hundred years old, and I tried and true, you know, Big Blue. You know, I ibm is this, you know, the big tanker, you know, it's not like they turn on a dime and you know, rapid pace of change. You think of IBM, you think of innovation. You think of, you know, trust. You think of all the innovations that have come out over the century. Plus do there and absolutely there is a little bit of impeded mismatch there and we'LL see So if ibm Khun truly let them do their own thing and not kind of merged suit groups and take over where the inertia of a larger group can slow things down I hope it will be successful But they're definitely our concerns And time will tell we'll see But you know analytics front You know, they just announced this morning Rehl eight Red hat enterprise linen, you know, just got announced and definitely something will be spent a lot of time So >> let's just jump in a relative Look again, We're gonna hear a little bit later on. We have several folks coming on board to talk aboutthe availability. Now what? What do you see from the outside? Looking at that. What is it going to allow you or us to do that? Seven Didn't know. Where did they improve? Is that on the automation side? Is it being maybe more attentive, Teo Hybrid environment or just What is it about? Really? That makes that special? >> Yes. So you know, first of all, you know these things take a while in the nice thing about being open sources. We've had transparency. If you wanted to know it was going to be in relate. You just look in the Colonel and and it's all out there. They've been working on this since twenty thirteen. Well, seven came out back in June of twenty fourteen. This has been a number of years in the mix. You know, security. The new, like crypto policy is a big piece that that's in their thie bullets that I got when I got the pre briefing on, It was, you know, faster and easier Deploy faster on boarding for non lennox users on, you know, seamless nondestructive migration from earlier versions of rail. So that's one of the things they really want to focus on is that it needs to be predictable, and I need to be able to move from one version the other. If you look at the cloud world, you know, when you don't go asking customers say, Hey, what version of Azure a ws are you running on your running on the latest and greatest? But if you look at traditional shrink wrap software, it was well, what virginity running? Well, I'm running in minus two and Why is that? Because I have to get it. I have to test it out. And then I, you know, find a time that I'm gonna roll that out, work it in my environment. So there is stability and understanding of the release cycle. My understanding is that they're going to do major releases every three years and minor releases every six months. So that cadence a little bit more like the cloud. And as I said, getting from one version a rail to the next should be easier and more non disruptive. Ah, a lot of people are going to want manage offerings where they don't really think about this. I have the latest version because that has not just the latest features but the latest security setting, which, of course, is a major piece of my infrastructure today to make sure that if there was some vulnerability released, I can't wait, You know, six or nine months for me to bake that in there. The limits community's always good have done a good job of getting fixes into it. But how fast can I roll that out into my environment is >> something I would assume that's that's a major factor in any consideration right now is is on the security front, because every day we hear about one more problem and these are just small little issues. These these air are could be multi billion dollar problems. But in terms of making products available today, how Muchmore important? How's that security shift? If you could put a percentage on it used to be, you know, axe and now it's X plus. I mean I mean, what kind of considerations are being given? >> You know what I'd say? Used to be that security got great lip service A. Said it was usually top of mind, but often towards bottom of budget. When you talk to administrators and you say, Oh, hey, where's your last security initiative? And that, like I've had that thing sitting on my desk for the last six months and I haven't had a chance to roll that out. I will get to it, but I want to again. If you go to that cloud operating model. If you talk about you know Dev, Ops movement is, I need to bake security into the process. If I'm doing C i D. It's not, I do something and then think about security afterwards. Security needs to be built in from the ground level. A CZ. You know, I I've heard people in the industry. Security is everyone's responsibility, and security must be baked in everywhere. So from the application all the way down to the chipset, we need to be thinking about security along the bar. Mind it is a board level discussion. Any user you talk too, you know, you don't say, Hey, where's the security sitting? Your priorities. You know, it's up there towards the top, if not vey top, because that's the thing that could put us out of business or, you know, definitely ruin careers. If if it doesn't go >> right, so there are there are probably a couple of platforms, every will or pillars. I think you like to call them that. You're looking forward to learning more about this week. I think in terms of red hats work one of those green hybrid cloud infrastructure, and we'LL get to the other to a little bit. But just your thoughts about how they're addressing that with the products that they offered the services they offer and where they're going in that >> Yeah, so look everything for red at start with rail. Everything is built on Lenox, and that's a good thing, because Lennox Endeavor is everywhere. If last year is that Microsoft ignite for the first time. And when you hear them talking a Microsoft talking about how Lennox is the majority of the environment, more than fifty percent of the environment are running linen goto a ws Same thing. All the cloud deployment Lennox is the preferred substrate underneath and Rehl doing very well to live in all those environment. So what we look at is, you know, some people say, is this olynyk show. It's like, well, at the core. Lin IX is the piece of it and relate the latest and greatest substantiation. But everywhere you go, there's going to be Lennox there from doing container ization. If a building on top of it with the the new cloud native models, it's there. And if you talk about how I get from my data center to a multi cloud environment, it's building things like Cooper Netease, which read that of course, uses open shift and you know those ties to eight of us and azure and you know, Google they're all there. So we mention Santina della's on stage tonight at Microsoft build. Yesterday there was announcement of this thing called Kita ke e d A, which has, like as your functions and ties in with open shift and spend a little time squinting it, trying to tease it apart. We've got some guests this week that'LL hopefully give some clarity, but it is. The answer is people today have multiple clouds and they have a lot of different ways they want. They want to do things, and Red has going to make sure that they help bridge the gap and simplify those environments across the board. Two years ago, when we were at the show big announcement about how open shift integrates with a W s so that if I'm using a ws But I want to have things in my environment still leverage some of those services. That was something that that Red had announced. I was, you know, quite impressed a time it was, you know, just last week being at the Del Show, it's V m. Where is the del strategy for how they get you know, A W, S, G, C, P and Azure and, you know, Red Hat does that themselves. Their software company. They live in all these cloud worlds, and therefore, open shift will help you extend from your data center through all of those public cloud environments on DH, you know? Yeah. So it's fascinating >> you've talked about Lennox to we're going to hear a little bit later on to about a fascinating the global economic study, that Red Hat Commission with the I. D. C. Of that talks about this ten trillion dollar impact of Lennox around the globe like to dive into that a little bit later on. >> Yeah, well, it's interesting, you know, it's the line I used is you say, and you say, Oh, well, how much impact is Lennox had? You know? You know, Red hats now, a three billion dollar company. That's good. But I was like, Okay, let's just take Google. You know, no slots of a company. Google underneath. It's not Red Hat Lennox, but Lennox is the foundation. I don't really think that Google could become the global search and advertising powerhouse they were. If it wasn't for Lennox to be able to help them get environment, there's a CZ we always talk with these technologies. You talk about Lennox, you talk about How do you talk about, you know, Cooper Netease? There are companies that will monetize it, but the real value is what business models and creation by. You know, all the enterprise is the service riders in the hyper scales that those technologies help enable. And that's where open source really shines is, you know, the order of magnitude network effect, that open source solutions have that its you say okay, three billion dollars? And is that what ten trillion dollars? It doesn't faze me, doesn't surprise me at all, but because my attention it look it. I'm not trying to trivialize. There's no But, you know, I've been watching clinics for twenty years, and I've seen the ripples of that effect. And if you dig down underneath your often finding it inside, >> I mentioned pillars that you were talking about cloud native development being another. But automation, let's just hit on that real quick before we head off on DH just again, with how that is being, I guess, highlighted. Or that's a central focus at and relate and and what automation? How that's playing in there I guess the new efficiencies they're trying to squeeze out. >> Yes. So? So what we always looked for it shows you're probably the last year is you know, you. How are they getting beyond the buzzwords? Aye, aye. When you talk about automation on area that that we've really enjoyed digging into is like robotic process automation. How do I take something that was manual? And maybe it was a fish injure? Not great. How can I make it perfectly efficient and use software robots to do that? So where are the places where I know that the amount of change and the scale and the growth that we have that I couldn't just put somebody to keyboard, you know, and have them typing or even a dashboard to be able to monitor and keep up with things? If I don't have the automation and intelligence in the system to manage things, I can't reach the scale and the growth that I need to. So where are you know, real solutions that are helping customers, you know, get over a little bit of the fear of Oh, my gosh, I'm losing a job. Or will this work or will this keep my business running and oh, my gosh, this will actually enabled me to be able to grow work on that security issue if I need to, rather than some of the other pieces and help really allow it agility to meet the requirements of what the business requires to help me move forward. So those are some of the things we kind of look across the shows. So, you know? Yeah. How much do we get? You know, buzzword, Bingo at the show. Where How much do we hear? You know, real customers with real solutions digging in and having, you know, new technologies that a couple of years ago would have had a saying, Wow, that's magic. >> But you say, Oh, my gosh. Yeah, and I don't want gosh right back with more. You're watching to serve the cube with the red had summit. We're in Boston, Massachusetts, that we'll be back with more coverage right after this