(upbeat music) >> Hello everyone. This is Dave Vellante with theCUBE. On October 28th, we'll be attending Commvault Connections '21. This is a premier industry event and it's focused on hybrid data services. The broadcast will be live from Commvault's Tinton Falls HQ. Now the agenda is packed with educational inspirational keynote speakers. For example, Dave Martin will be speaking. He is in the global chief security office at ADP, Stephen Orban of AWS and Dave Taunton of Microsoft will be sharing insights. And of course Commvault CEO Sanjay Mirchandani, he's a long-time guest of theCUBE and a rare example of a CIO transitioning to a CEO role and having excellent success with Commvault transformation. These sessions that are referencing will engage you on topics like ransomware, SaaS, and hybrid cloud, and more there's something for every data professional. And by attending, you have the chance to have an exclusive consultation with the dev team at Commvault, which is always a hot ticket item. Now you can catch all the action live on SiliconANGLE and thecube.net so go right now, register for connections '21, it takes less than a minute. I just did it. We'll see you there. (upbeat music)

>> Narrator: From around the globe, it's the CUBE with digital coverage of Smart Data Marketplaces, brought to you by Io-Tahoe. >> Hello this is Dave Vellante of the CUBE inviting you to join me for a special drill down presentation on the importance of automated data migration. Along with our friends from Io-Tahoe, we're going to explore the recent trends of automated data discovery, adaptive data governance, and just how far we've come from manually curating an enterprise data catalog. Ajay Vahora is the CEO of Io-Tahoe, as well as Stuti Deshpande of AWS and the digital evangelist Ved Sen of TCS, Tata Consultancy Services, will be there as well. Hope you can join us on Thursday, September 17th, at 9:00 a.m. Pacific for Smart Data Marketplaces. For more details, click on theCUBE.net. (upbeat music)

>>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.

>> As we enter the new reality of hybrid work, the exposures that companies have faced as a result of the pandemic and consequent shifts in technology strategies, demand new ways to protect data. We heard today from INFINIDAT and one of its key partners, Mark Three Systems, how these two companies are helping customers respond to this threat. Key factors to consider are how to back up data as quickly as possible. And at the same time, isolate critical data in the case where hackers are attempting to hold your data hostage. Now in that instance, it's critical to have an isolated, safe copy of the data because as the saying goes, backup is one thing, but fast recovery is everything. This is Dave Vellante for the Cube. Thanks for watching. >> Keep rolling, Alex. Do one more day just as a safe copy. >> You got it. >> A little more, little more energy in your face, your hands, your pits. >> You bet. I don't know. You bet. Okay. Ready, Alex? As we enter the new reality of hybrid work, the exposures that companies have faced as a result of the pandemic and their consequent shift in technology strategies and spending it demand new ways to protect data. Now we heard today from INFINIDAT and one of its key partners, Mark Three Systems, how these two companies are coming together to help customers respond to new threats. Now key factors that customers should consider are how to backup data as quickly as possible. And at the same time isolate critical data in the case where hackers are attempting to hold your data hostage. In that instance it's critical to have an isolated saved copy of the data because as the saying goes, backup is one thing, but fast recovery, that's everything. This is Dave Vellante for the Cube. Thanks for watching.

>> Okay listen, we're gearing up for the start of the Snowflake Data Cloud Summit. And we want to go back to the early roots of Snowflake. We got some of the founding engineers here, Abdul Munir, Ashish Motivala and Allison Lee. They're three individuals that were at Snowflake, in the early years and participated in many of the technical decisions. That led to the platform and is making Snowflake famous today. Folks great to see you. Thanks so much for taking some time out of your busy schedules. >> Thank you for having us- >> Same. >> It's got to be really gratifying to see this platform that you've built, taking off and changing businesses. So I'm sure it was always smooth sailing, right? There were no debates, where there ever? >> I've never seen an engineer get into a debate. >> Yeah alright, so seriously. So take us back to the early days, you guys choose whoever wants to start, but what was it like, early on we're talking 2013 here, right? >> That's right. >> When I think back to the early days of Snowflake. I just think of all of us sitting in one room at the time, we just had an office that was one room with 12 or 13 engineers sitting there, clacking away at our keyboards, working really hard, churning out code punctuated by somebody asking a question about hey, what should we do about this? Or what should we do about that? And then everyone kind of looking up from their keyboards and getting into discussions and debates about the work that we were doing. >> So, Abdul was it just kind of heads down, headphones on just coding or? >> I think there was a lot of talking and followed by a lot of typing. And I think there were periods of time where anyone could just walk in into the office and probably out of the office and all they'd hear is probably people typing away their keyboards. And one of my most vivid memory is actually I used to sit right across from Allison and there was these two huge monitors between us. And I would just hear her typing away at her keyboard. And sometimes I was thinking and all that typing got me nervous because it seemed like Allison knew exactly what she needed to do. And I was just still thinking about it. >> So Ashish was this like bliss for you as a developer or an engineer? Or was it a stressful time? What was the mood? >> Then when you don't have a whole lot of customers, there's a lot of bliss, but at the same time, there's a lot of pressure on us to make sure that we build the product. There was a timeline ahead of us. We knew we had to build this in a certain timeframe. So one thing I'll add to what Allison and Abdul said is, we did a lot of whiteboarding as well. There were a lot of discussions and those discussions were a lot of fun. They actually cemented what we wanted to build. They made sure everyone was in tune and there we have it. >> Yeah, it is a really exciting time. We can do it any start-up. When you have to make decisions in development and variably you come to a fork in the road. So I'm curious as to what some of those forks might've been, how you guys decided which fork to take. Was there a Yoda in the room that served as the Jedi Master? How are those decisions made? Maybe you could talk about that a little bit. >> That's an interesting question. And as I think back one of the memories that sticks out in my mind is this epic meeting in one of our conference rooms called Northstar and many of our conference rooms are named after ski resorts because the founders are really into skiing. And that's where the Snowflake name comes from. So there was this epic meeting and I'm not even sure exactly what topic we were discussing. I think it was the sign up flow and there were a few different options on the table. And one of the options that people were gravitating to, one of the founders didn't like it. And they said a few times that this makes no sense. There's no other system in the world that does it this way. And I think one of the other founders said, that's exactly why we should do it this way or at least seriously consider this option. So, I think there was always this tendency and this impulse that we needed to think big and think differently and not see the world the way it is, but the way we wanted it to be and then work our way backwards and try to make it happen. >> Allison, any fork in the road moments that you remember? >> Well, I'm just thinking back to a really early meeting with Ashish and a few of our founders where we're debating something probably not super exciting to a lot of people outside of hardcore database people, which was how to represent our column metadata. And I think it's funny that you that you mentioned Yoda, because we often make jokes about one of our founders Thierry and referred to him as Yoda, because he has this tendency to say very concise things that kind of make you scratch your head and say, wow, why didn't I think of that? Or what exactly does that mean? I never thought about it that way. So, when I think of the Yoda in the room, it was definitely Thierry, >> Ashish is there anything you can add to this conversation? >> I'll agree with Allison on the Yoda comment for sure. Another big fork in the road I recall was when we changed one of our meadow store, where we store and are willing to try and metadata. We used to use a tool called my SQL and we changed it to another database called foundation DV. I think that was a big game changer for us. And it was a tough decision. It took us a long time, for the longest time we even had our own little branch it was called foundation DV and everybody was developing on that branch, it's a little embarrassing but those are the kinds of decisions that have altered the shape of Snowflake. >> Yeah, these are really down in the weeds hardcore stuff that a lot of people might not be exposed to. What would you say was the least obvious technical decision that you had to make at the time? And I want to ask you about the most obvious too, but what was the one that was so out of the box? You kind of maybe mentioned it a little bit before, but I wonder if we could double click on that? >> Well, I think one of the core decisions in our architecture is the separation of compute and storage that is really core to our architecture. And there's so many features that we have today, for instance data sharing, zero-copy cloning, that we couldn't have without that architecture. And I think it was both not obvious. And when we told people about it in the early days, there was definitely skepticism about being able to make that work and being able to have that architecture and still get great performance. >> Exactly- >> Yeah, anything that was like clearly obvious, maybe that was the least and the most that separation from compute and store, 'cause it allowed you to actually take advantage of cloud native, but was there an obvious one that is it sort of dogma that you philosophically live behind to this day? >> I think one really obvious thing is the sort of no tuning, no knobs, ease of use story behind Snowflake. And I say it's really obvious because everybody wants their system to be easy to use. But then I would say there were tons of decisions behind that, that it's not always obvious the implications of such a choice, right? And really sticking to that. And I think that that's really like a core principle behind Snowflake that led to a lot of non-obvious decisions as a result of sticking to that principle. >> To wrap to that now you've gotten us thinking, I think another really interesting one was really, should we start from scratch or should we use something that already exists and build on top of that. And I think that was one of these almost philosophical kind of stances that we took, that a lot of the systems that were out there were the way they were because they weren't built for the platforms that they were running on. And the big thing that we were targeting was the cloud. And so one of the big stances we took was that we were going to build it from scratch and we weren't going to borrow a single line of code from any other database out there. And this was something that really shocked a lot of people and many times that this was pretty crazy. And it was, but this is how you build great products. >> That's awesome, all right, Ashish give your last word, we got like just 30 seconds left take, bring us home. >> Till date actually one of those that shocks people when you talk to them and they say, wow, you're not really using any other database? And you build this entirely yourself? The number of people who actually can build a database from scratch are fairly limited. The group is fairly small. And so it was really a humongous task. And as you've mentioned, it really changed the direction of how we designed the database. What does the database really mean to us, right? The way Snowflake has built a database, it's really a number of organs that come together and form the body. And that's also a concept that's novel to the database industry. >> Guys congratulations, you must be so proud and it's going to be awesome watching the next decade. So thank you so much for sharing your stories. >> Thanks Dave. >> Thank you- >> Thank you.

>>from the Cube Studios in Palo Alto in Boston, connecting with thought leaders around the globe. These are cloud native insights. Hi, I'm stew Minimum and the host of Cloud Native Insights. And the threat that we've been pulling on with Cloud Native is that we needed to be able to take advantage of the innovation and agility that cloud in the ecosystem around it can bring, not just the location. It's It's not just the journey, but how do I take advantage of something today and keep being able to move for Happy to welcome back to the program one of our regulars and someone that I've had lots of discussion about? Cloud Cloud. Native Serverless So Cory Quinn, the Keith Cloud economists at the Duck Bill Group. Corey, always good to see you. Thanks for joining us. >>It is great to see me. And I always love having the opportunity to share my terrible opinions with people who then find themselves tarred by the mere association. And there's certainly no exception to use, too. Thanks for having me back. Although I question your judgment. >>Yeah, you know, what was that? Pandora's box. I open when I was like Hey, Corey, let's try you on video so much. And if people go out, they can look at your feet and you've spent lots of money on equipment. You have a nice looking set up. I guess you missed that one window of opportunity to get your hair cut in San Francisco during the pandemic. But be doesn't may Corey, why don't you give our audience just the update You went from a solo or mentor of the cloud? First you have a partner and a few other people, and you're now you've got economists. >>Yes, it comes down to separating out. What I'm doing with my nonsense from other people's other people's careers might very well be impacted by it considered tweet of mine. When you start having other clouds, economists and realize, okay, this is no longer just me we're talking about here. It forces a few changes. I was told one day that I would not be the chief economist. I smile drug put on a backlog item to order a new business cards because it's not like we're going to a lot of events these days, and from my perspective, things continue mostly a base. The back. To pretend people now means that there's things that my company does that I'm no longer directly involved with, which is a relief, that absolutely, ever. But it's been an interesting right. It's always strange. Is the number one thing that people who start businesses say is that if they knew what they were getting into, they'd never do it again. I'm starting to understand that. >>Yeah, well, Corey, as I mentioned you, and I have had lots of discussions about Cloud about multi Cloud server. Listen, like when you wrote an article talking about multi cloud is a worse practice. One of the things underneath is when I'm using cloud. I should really be able to leverage that cloud. One of the concerns that when you and I did a cube con and cloud native con is does multi cloud become a least common denominator? And a comment that I heard you say was if I'm just using cloud and the very basic services of it, you know, why don't I go to an AWS or an azure which have hundreds of services? Maybe I could just find something that is, you know, less expensive because I'm basically thinking of it as my server somewhere else. Which, of course, cloud is much more than so you do with a lot of very large companies that help them with their bills. What difference there differentiates the companies that get advantage from the cloud versus those that just kind of fit in another location, >>largely the stories that they tell themselves internally and how they wind up adapting to cloud. If the reason I got into my whole feel about why multi cloud is a worst practice is that of you best practices a sensible defaults, I view multi cloud as a ridiculous default. Sure, there are cases where it's important, and so I don't say I'm not suggesting for a second that those people who are deciding to go down that are necessarily making wrong decisions. But when you're building something from scratch with this idea toward taking a single workload and deploying it anywhere in almost every case, it's the wrong decision. Yes, there are going to be some workloads that are better suited. Other places. If we're talking about SAS, including that in the giant wrapper of cloud definition in terms of what was then, sure you would be nuts to wind of running on AWS and then decide you're also going to go with codecommit instead of git Hub. That's not something sensible people to use get up or got sick. But when I am suggesting, is that the idea of building absolutely every piece of infrastructure in a way that avoids any of the differentiated offerings that your primary cloud provider uses is just generally not a great occasionally you need to. But that's not the common case, and people are believing that it is >>well, and I'd like to dig a little deeper. Some of those differentiated services out there there are concerned, but some that said, You know, I think back to the past model. I want to build something. I can have it live ever anywhere. But those differentiated services are something that I should be able to get value out of it. So do you have any examples, or are there certain services that you have his favorites that you've seen customers use? And they say, Wow, it's it's something that is effective. It's something that is affordable, and I can get great value out of this because I didn't have to build it. And all of these hyper scaler have lots of engineers built, building lots of cool things. And I want to take advantage of that innovation. >>Sure, that's most of them. If we're being perfectly honest, there are remarkably few services that have no valid use cases for no customer anywhere. A lot of these solve an awful lot of pain that customers have. Dynamodb is a good example of this Is that one a lot of folks can relate to. It's super fast, charges you for what you use, and that is generally yet or a provision Great. But you don't have to worry about instances. You have to worry about scaling up or scaling down in the traditional sense. And that's great. The problem is, is great. How do I migrate off of this on to something else? Well, that's a good question. And if that is something that you need to at least have a theoretical exodus for, maybe Dynamo DV is the wrong service for you to pick your data store personally. If I have to build for a migration in mind on no sequel basis, I'll pick mongo DB every time, not because it's any easier to move it, but because it's so good at losing data, that'll have remarkably little bit left. Migrate. >>Yeah, Corey, of course. One of the things that you help customers with quite a bit is on the financial side of it. And one of the challenges if I moved from my environment and I move to the public cloud, is how do I take advantage not only of the capability to the cloud but the finances of the cloud. I've talked to many customers that when you modernize your pull things apart, maybe you start leveraging serverless capabilities. And if I tune things properly, I can have a much more affordable solution versus that. I just took my stuff and just shoved it all in the cloud kind of a traditional lift and shift. I might not have good economics. When I get to the cloud. What do you see along those lines? >>I'd say you're absolutely right with that assessment. If you are looking at hitting break even on your cloud migration in anything less than five years, it's probably wrong. The reason to go to Cloud is not to save money. There are edge cases where it makes sense, Sure, but by and large you're going to wind up spending longer in the in between state that you would believe eventually you're going to give up and call it hybrid game over. And at some point, if you stall long enough, you'll find that the cloud talent starts reaching out of your company. At which point that Okay, great. Now we're stuck in this scenario because no one wants to come in and finish the job is harder than we thought we landed. But it becomes this story of not being able to forecast what the economics are going to look like in advanced, largely because people don't understand where their workloads start and stop what the failure modes look like and how that's going to manifest itself in a cloud provider environment. That's why lift and shift is popular. People hate, lift and ship. It's a terrible direction to go in. Yeah, so are all the directions you can go in as far as migrating, short of burning it to the ground for insurance money and starting over, you've gotta have a way to get from where you are, where you're going. Otherwise, migration to be super simple. People with five weeks of experience and a certification consult that problem. It's but how do you take what's existing migrated end without causing massive outages or cost of fronts? It's harder than it looks. >>Well, okay, I remember Corey a few years ago when I talk to customers that were using AWS. Ah, common complaint was we had to dedicate an engineer just to look at the finances of what's happening. One of the early episodes I did of Cloud Native Insights talked to a company that was embracing this term called Been Ops. We have the finance team and the engineering team, not just looking back at the last quarter, but planning understanding what the engineering impacts were going forward so that the developers, while they don't need tohave all the spreadsheets and everything else, they understand what they architect and what the impact will be on the finance side. What are you hearing from your customers out there? What guidance do you give from an organizational standpoint as to how they make sure that their bill doesn't get ridiculous? >>Well, the term fin ops is a bit of a red herring in there because people immediately equate it back to cloud ability before their app. Geo acquisitions where the fin ops foundation vendors are not allowed to join except us, and it became effectively a marketing exercise that was incredibly poorly executed in sort of poisoned the well. Now the finance foundations been handed off to the Cloud Native Beauty Foundation slash Lennox Foundation. Maybe that's going to be rehabilitated, but we'll have to find out. One argument I made for a while was that developers do not need to know what the economic model in the cloud is going to be. As a general rule, I would stand by that. Now someone at your company needs to be able to have those conversations of understanding the ins and outs of various costs models. At some point you hit a point of complexity we're bringing in. Experts solve specific problems because it makes sense. But every developer you have does not need to sit with 3 to 5 days course understanding the economics of the cloud. Most of what they need to know if it's on a business card, it's on an index card or something small that is carplay and consult business and other index ramos. But the point is, is great. Big things cost more than small things. You're not charged for what you use your charger for. What you forget to turn off and being able to predict your usage model in advance is important and save money. Data transfers Weird. There are a bunch of edge cases, little slice it and ribbons, but inbound data transfer is generally free. Outbound, generally Austin arm and a leg and architect accordingly. But by and large for most development product teams, it's built something and see if it works first. We can always come back later and optimize costs as you wind up maturing the product offering. >>Yeah, Cory, it's some of those sharp edges I've love learning about in your newsletter or some of your online activities there, such as you talked about those egress fees. I know you've got a nice diagram that helps explain if you do this, it costs a lot of money. If you do this, it's gonna cost you. It cost you a lot less money. Um, you know, even something like serverless is something that in general looks like. It should be relatively expensive, but if you do something wrong, it could all of a sudden cost you a lot of money. You feel that companies are having a better understanding so that they don't just one month say, Oh my God, the CFO called us up because it was a big mistake or, you know, where are we along that maturation of cloud being a little bit more predictable? >>Unfortunately, no. Where near I'd like us to be it. The story that I think gets missed is that when you're month over, month span is 20% higher. Finance has a bunch of questions, but if they were somehow 20% lower, they have those same questions. They're trying to build out predictive models that align. They're not saying you're spending too much money, although by the time the issues of the game, yeah, it's instead help us understand and predict what's happening now. Server less is a great story around that, because you can tie charges back to individual transactions and that's great. Except find me a company that's doing that where the resulting bill isn't hilariously inconsequential. A cloud guru Before they bought Lennox, I can't get on stage and talk about this. It serverless kind of every year, but how? They're spending $600 a month in Lambda, and they have now well, over 100 employees. Yeah, no one cares about that money. You can trace the flow of capital all you want, but it grounds up to No one cares at some point that changes. But there's usually going to be far bigger fish to front with their case, I would imagine, given, you know, stream video, they're probably gonna have some data transfer questions that come into play long before we talk about their compute. >>Yeah, um, what else? Cory, when you look at the innovation in the cloud, are there things that common patterns that you see that customers are missing? Some of the opportunities there? How does the customers that you talk to, you know, other than reading your newsletter, talking Teoh their systems integrator or partner? How are they doing it? Keeping up with just the massive amount of change that happens out >>there. Get customers. AWS employees follow the newsletter specifically to figure out what's going on. We've long since passed a Rubicon where I can talk incredibly convincingly about services that don't really exist. And Amazon employees won't call me out on the joke that I've worked in there because what the world could ever say that and then single. It's well beyond any one person's ability to keep it all in their head. So what? We're increasingly seeing even one provider, let alone the rest. Their events are outpacing them and no one is keeping up. And now there's the persistent, never growing worry that there's something that just came out that could absolutely change your business for the better. And you'll never know about it because you're too busy trying to keep up with all the other number. Every release the cloud provider does is important to someone but none of its important everyone. >>Yeah, Corey, that's such a good point. When you've been using tools where you understand a certain way of doing things, how do you know that there's not a much better way of doing it? So, yeah, I guess the question is, you know, there's so much out there. How do people make sure that they're not getting left behind or, you know, keep their their their understanding of what might be able to be used >>the right answer. There, frankly, is to pick a direction and go in it. You can wind up in analysis paralysis issues very easily. And if you talk about what you've done on the Internet, the number one responsible to get immediately is someone suggesting an alternate approach you could have taken on day one. There is no one path forward for any six, and you can second guess yourself that the problem is that you have to pick a direction and go in it. Make sure it makes sense. Make sure the lines talk to people who know what's going on in the space and validate it out. But you're going to come up with a plan right head in that direction, I assure you, you are probably not the only person doing it unless you're using. Route 53 is a database. >>You know, it's an interesting thing. Corey used to be said that the best time to start a project was a year ago. But you can't turn back time, so you should start it now. I've been saying for the last few years the best time to start something would be a year from now, so you can take advantage of the latest things, but you can't wait a year, so you need to start now. So how how do you make sure you maintain flexibility but can keep moving projects moving forward? E think you touched on that with some of the analysis paralysis, Anything else as to just how do you make sure you're actually making the right bets and not going down? Some, you know, odd tangent that ends up being a debt. >>In my experience, the biggest problem people have with getting there is that they don't stop first to figure out alright a year from now. If this project has succeeded or failed, how will we know they wind up building these things and keeping them in place forever, despite the fact that cost more money to run than they bring in? In many cases, it's figure out what success looks like. Figure out what failure looks like. And if it isn't working, cut it. Otherwise, you're gonna wind up, went into this thing that you've got to support in perpetuity. One example of that one extreme is AWS. They famously never turn anything off. Google on the other spectrum turns things off as a core competence. Most folks wind up somewhere in the middle, but understand that right now between what? The day I start building this today and the time that this one's of working down the road. Well, great. There's a lot that needs to happen to make sure this is a viable business, and none of that is going to come down to, you know, build it on top of kubernetes. It's going to come down. Is its solving a problem for your customers? Are people they're people in to pay for the enhancement. Anytime you say yes to that project, you're saying no to a bunch of others. Opportunity Cost is a huge thing. >>Yeah, so it's such an important point, Cory. It's so fundamental when you look at what what cloud should enable is, I should be able to try more things. I should be able to fail fast on, and I shouldn't have to think about, you know, some cost nearly as much as I would in the past. We want to give you the final word as you look out in the cloud. Any you know, practices, guidelines, you can give practitioners out there as to make sure that they are taking advantage of the innovation that's available out there on being able to move their company just a little bit faster. >>Sure, by and large, for the practitioners out there, if you're rolling something out that you do not understand, that's usually a red flag. That's been my problem, to be blunt with kubernetes or an awful lot of the use cases that people effectively shove it into. What are you doing? What if the business problem you're trying to solve and you understand all of its different ways that it can fail in the ways that will help you succeed? In many cases, it is stupendous overkill for the scale of problem most people are throwing. It is not a multi cloud answer. It is not the way that everyone is going to be doing it or they'll make fun of you under resume. Remember, you just assume your own ego. In this sense, you need to deliver an outcome. You don't need to improve your own resume at the expense of your employer's business. One would hope, >>Well, Cory, always a pleasure catching up with you. Thanks so much for joining me on the cloud. Native insights. Thank you. Alright. Be sure to check out silicon angle dot com if you click on the cloud. There's a whole second for cloud Native insights on your host to minimum. And I look forward to hearing more from you and your cloud Native insights Yeah, yeah, yeah, yeah, yeah.

>> Live from Las Vegas, It's theCube. Covering VMworld 2018. Brought to you by VMware and it's ecosystem partners. >> Welcome back, this is day three of three days live wall to wall coverage of VMworld 2018. This is theCube, I'm Stu Miniman, and my co-host this morning is Justin Warren. How about I welcome back to our program two Cube Alum's from the VMVare storage and availity business unit. Yanbing Li, second time in The Cube this week, is the senior vice president >> Yes. >> and general manager of the group. And Christos Karamanolis, is the fellow and CTO, thank you both for joining us. >> Great to be here. >> Great to be here. >> Alright, so first of all, congratulations. A lot of news this week, a lot of excitement around it. And we're talking off cameras, there's so much there that people don't understand some of the work that went into this. And some highlights as to things that I know VMWare thinks will be very game changing over the next couple of years. So, we're excited to dig into this. Yanbing, why don't you start us off with a little bit of an overview from your group as to the news this week. >> Yeah, happy to do that. I think, so, we are seeing a lot of customer energy around what we're doing in storage and availability. You know, there's huge momentum behind product like vSan and our customers are truly embracing HCI in very mainstream use cases, and we've seen customer after customer have gone all in, meaning they're taking HCI and made a determination to run that for all of their virtualized workload. So, very exciting time. But what's more interesting is their expanded view on what HCI is about. Certainly, we started with virtualizing computer and storage together on servers. But we're seeing rapid expansion of that definition. You know, we've been a believer that HCI is foundationally a software lab architecture. I think know, there's more recognition in that. And it's also going from just computers and storage to the full stack of the entire software defined data center. It's expanding into the cloud, as you've seen from VMCI WS. It's expanding to the edge, expanding from just traditional apps to cloud native apps. You know, we've announced beta for vSan to become the storage platform for Kubernetes' Navisphere environment. So, a lot of exciting expansion around how customers want to see HCI. And if you look at HCI, hybrid cloud, SDDC, the boundary around these three is not very very clear. I think they're all converging to work, something that's very common. >> Yeah, Christos? I want you to help unpack this a little bit for us. I remember speaking to you a couple of years ago, and your team. We know how many years of effort went into, set the ground work for vSan. with the underlying things that arrived with the API's, and development with your partner ecosystem. Taking vSan as a foundation... Oh, it's going to work with Kubernetes and cloud and everything. It's not a simple port, like, you know, no offense to the hardware people, but putting it on a new platform? Alright, you need to test it, integrate it, make it a couple tweaks, but. The software level, there's a lot of things that go on here. Talk about what the team's been working on, some of the big architectural things that've been happening. >> Oh, yes, absolutely. There are some fundamental changes. We never stop, we never declare that we have finished what we are doing. Obviously, the world is changing around us. Not only the hardware, as you know. There are many important changes there, with NVMe becoming now very prevalent, and renewed aero-technologies appearing, like persistent memory. But, for us, a focal point the last year or so has been, how do we move our entire software stack data on being outlined earlier, into any type of environment, including public clouds? So, you see now, with a few more clouds in AWS, the customers can run applications there without having to re-platform them. It's the exact same environment. So, a keystone of that environment is the storage. How do you virtualize storage? How do you deal with any type of infrastructure? So, vSan was developed for physical devices, SS disc and magnetic disc, more recently NVMe. Now, what we want to give is the option to our customers to use the cost efficiencies of cloud storage. Without the those sacrificing the semantics, the properties the vSphere stack. So, we did a lot of engineering to make vSan work on top of EBS. So, it may sound simple when you announce it at the keynote of VMWorld, but it took lot of hard engineering to adapt a platform. vSphere and vSan was designed for physical hardware, do not work on virtual storage volume. So, that is just one example, there are more examples. For cloud-native use cases, as you said. >> Yeah, I don't think people quite understand the implications of that. The fact that you can use things in the same way in multiple different locations, the whole idea behind multi-cloud-- If you can operate it in the same way as you can on site as you can in whichever cloud you choose. For enterprises who are used to doing things one way, and have made big investments in VMWare, this just opens up an entire universe of opportunity for them. >> Absolutely, and you get the best of both worlds, right? You have the same operational model, the same characteristics I can run now on Amazon applications that use vSphere, ETSI, or the motion pictures that require cell storage. On the cloud, you do not have cell storage. EBS volumes can be accessed by one host at a time, and like stores that need the networks, and vSan brings those stores their networks and semantics, all in software of course, on the cloud. So, I can run my traditional applications, as well as some new generation applications. And for us, strategically, what we've done with EBS? If you think about that is one step into a much bolder vision where vSan becomes this common storage platform that virtualize any type of storage. Physical, or cloud, or virtual, so we expose the same operational model, and the same store semantics to all those who run these three platforms. And this is, you know, just one step. >> And it's not how you-- there is the common operation model that's very appealing to all the enterprise customers. But we are truly marrying the strength and the capabilities of vSan and vSphere and the VMR platform was what EBS uniquely provide. That's elasticity, scalability, but you know, we have a much richer set of data services that we've already viewed into the whole VMR stack. >> Yeah, Yanbing, you bring up some really interesting points. When we put our critical analysis hat on, when the partnership was announced. It was like, "Well, Amazon's got access to 500,000 "VMWare customers, we're going to start "getting customers comfortable with Amazon. Great, they can start moving over." The thing that really caught a lot our attention is, it's some of the Amazon services that are now coming to the VMWare customers. So, EBS is a really good one. When you talk about, you know, the database capabilities that Amazon has, that now I can do on premises, this is a partnership, a two-way street. Its not, you know, just a one way. Maybe speak a little bit about that maturation, and, you know, definitely want to get from Christos, also. There's questions about some of the technical ways of how that works. >> Yeah, what I'm excited is exactly what you described. This is not a one way street, it's really bi-directional. And the levels of collaboration is not just superficial. It's deep levels of integration and leveraging each other to strength, in terms of both technology as well as customer reach. I think that what make the partnership is, you know, people can see that is taking to whole new level. And Christos has been very deeply involved with the various solution architects, and when we examine how we take RDS back on Prime to a VMR environment, I think he can tell a lot more stories behind that. >> For us, actually, it was a great learning experience, I must admit. Because, obviously, we see strongly the desire for our classroom is to start moving from managing the low level, nitty gritty details of the physical IT infrastructure, which we were, you know, traditionally helping them to do, to moving up the starter. Many of them now, they want to have their own users, their own customers, internal customers, to run all those applications. And what are the most critical components of business critical applications? They are the databases, right? So, how can we make the life of our customers easier, how can we provide them the tools to offer data, databases, as a service to their own users? So, this has been our high level objective, and of course, our partnership with AWS helps us deliver some of those properties. >> Christos, I want you to go one level deeper for us. Because some people it's like, >> I'd be happy to. "Wait, RDS, that's, you know, the cool new databases "in Amazon. Wait, I can do something on--" Is that an extension, am I putting things back and forth? Those of us that lived through the virtualization were getting databases just virtualized took years and a lot of hard work. And, I can't just have a database spanning between these, and moving back and forth. This isn't, you know, -- We haven't broken the laws of physics. >> We have not, because here-- >> Help us explain >> What is and isn't possible today. >> Absolutely. First of all, let me highlight what are the main pain points of customers. It's one thing to set up your application and install it and run it. But then there are all the day two operations, right? How do you patch the software, the operating system, the database? How do you scale it, up or down? How do you, even more to the performance, how do you do data protection, backup, disaster recovery? Those are really painful, difficult tasks, that involve a lot of work from expert database administrators that they'd rather be doing some of the important things that address the business earnings, right? So, our objective is to address this. Now, to your point, how do we, you know? What about those laws of physics? How can we have services on the cloud and service on a premise? What we announce here, this RDS, Relational Database Services, on VMWare, it is a fully stand alone service that runs on VMWare environment on premises. There are no dependencies on the public cloud, you have your data sets on your own data centers, and this is actually a major requirement of customers. Whether it's for compliance reasons, or security, or company policy, we insure that your data stays in your data center, while you still get all the benefits of a managed database that you don't need to do all those, you know, little tedious operational tasks I mentioned earlier. Moreover, we support data protection using, actually, underlying vSphere features. Like ETSI and clustering, or even data protection by creating copies of your database in another available domain within your data center. And this is a lot of work that VMWare did to make this happen, as you can imagine. So, that's a lot of infrastructural work, but we support the full range of features that you get on AWS, without having to go over the wire and, you know, break those laws of physics. >> I don't think people have quite understood how profound that is. We're here at a VMWare show, I've spent a lot of time with developers, and the developers are going to love this. Because, now they can use exactly the same way that they operate in public cloud, which they've loved for many years. Being able to do that on site? The way application development is going to happen inside enterprises, where they want to keep it on site, they want to keep it under they're own control, they want their data secured inside their own data centers. The ability for them to do that, and still develop applications in the same way that they could as cloud-native? Cloud-native now means that it runs on site. This is going to be amazing. >> Absolutely. Our customers explicitly tell us that they want to consume, not storage, but data. Those abstractions that matter to the application. So much so, that they have been asking us already, "Hmmm, what is next?", right? "Can you offer us some of this new generation databases?", you know, "the Mongoose or the Cassandra's of the world? "Can we have some similar experience with those "because they're very painful to deploy "and manage in the data centers." So, I cannot make any commitment, of course, but this is an indication of how much interest there is in this type of services. >> Yeah, it really does show you, I think, some of the strategic intent from VMWare. And this is a very clear move for what is going to be possible for customers to actually be able to do on site, it's really quite exciting. >> And for us, you know. Our role providing all the storage related capability, and we've been strongly expanding our application footprint to cover the Hadoop, the Cassandra, the Mango DV type of application as well as containerize the applications. And, you know, we have introduced a lot of new capability or solution that address exactly like that. >> Containerize the applications, for example, against the announcement, I think, didn't receive the attention, that in my opinion, it deserved is supporting natively in vSphere, and with vSan, specifically, cloud-native use cases. Actually, we're introducing a controlled playing, and expanding our store's controlled playing, to manage natively, container volumes. So, now, the same way today, our customers can visit builders through the UI or API's, and have management workflows for virtual machines and virtual disc, VMDK's. Now, they can also manage volumes of containers. And, as you've heard also, we are working with Kubernetes being our main focal point and with PKS to support natively Kubernetes on vSphere, down the road. >> Yeah, great point. I wonder, since we're talking about storage here, you've talked about Kubernetes, we talked about what's in the cloud and on premises. Give us the updated view how VMWare views and how you're helping customers with-- Data can't-- I can't just move, you know, data anywhere, so. While it's good to have similar frameworks, and different-- similar tools there, but still, where data lives, what I move, how I move it, do I move it, how that whole, kind of, data locality is seen today? >> The answer, we have been very keen in defining what we doing in the broader category of data management. From data mobility to protection to analytics, and to life cycle management, the whole slew of that. And we've been starting by building a lot of-- First of all, our job is to make vSan a storage platform that can enable these different demands of data. So, we've expanded vSan's roll from purely from delivering block storage now to offer file, and down the road, object. Cuz a lot of the new data will be consumed in an object like format. And we've also been painting our roadmap for the broader data management, so. >> Yes, exactly. On one hand, we'll provide the platform for primary storage that serves all the needs of the applications, block, file, object, we may even consider a native file interface, actually, for zero data copies, since you were asking about the technical details. I'm very excited about that, you know. We'll see, some of these things will come in the future. But, then, given that you have the platform, what you are building on top of that is data mobility and data protection workflows that are driven by policies. The very first step in that direction is our disaster recovery as a service we offer for hybrid clouds. There, the new model is that, even how you manage your data is as a service. Not a traditional model of installing software and a hundred different bits and pieces that have to integrate with each other and operate. Very simple, you go to a portal, and you manage your data, in this case, starting with disaster recovery use cases. You specify policies, like recovery point objectives. Down the road you may also give the options for recover time objectives. And, also, specify, by policies, what of your data want to be archived and stay on your data center, what of the data can go to the public cloud through your, you know, the hybrid models of cloud model we offer. So, our goal down the road is quite ambitious in offering comprehensive, uniform data management across clouds, that goes all the way from the edge, your Motofy's, your oil rig, all the way to the enterprise, the Cassandra's, to the hybrid clouds. And data mobility there is, you know, using our data transport, our archival capabilities that are coming with vSan Native Snapshot that we also announced at this VMWorld. These will give you the ability to manage your data across all those environments. >> Alright, so, last thing I just want to say. It's interesting to watch this space because we say there's a lot happening under the scenes that people don't understand. I was seeing some research lately saying where AWS lives in the storage ecosystem. I've written an article, couple a years ago. They were the quiet, billion dollar, you know, storage company. And one analyst firm said,"Oh, they're number 3, "and they'll be number 1 in storage." Wikibon actually published a report this month talking about what we call true private cloud. And in our support where we look at the software ecosystem, Yanbing, do you remember who we had number 1 on the list there when you picked >> Ah, yeah... software plus the ecosystem around there for -- >> I remember it clearly, you said it's VMWare. >> Yeah, so, you know, it surprises some people when you look it there, but I'm sure it's no surprise to you and your team, I'm sure. >> So, you know what we've started with vSan is quickly becoming a big way of how all of vSphere customers consume storage. And certainly, that has been our initial focus. But what we are doing for the cloud, what we are doing for the next generation applications. I think we are re-imagining a lot of the things. And it's great to have people like Christos, who started this journey many many years ago, and continue to expand our horizon. Yeah, this is an exciting time for our business unit, and certainly for VMWare, and our customers. >> Christos, in the end, really appreciate us being able to geek out, dig into some of the really important innovations happening in this space. For Justin Warren, I'm Stu Miniman, still a full third day live coverage here from VMWorld 2018, thanks for watching theCube.

>> Narrator: Live from Washington D.C., it's theCUBE covering .conf2017 Brought to you by Splunk. (techno music) >> Well, welcome back to Washington D.C. We're at the Walter Washington Convention Center as we wrap up our coverage here of .conf2017. As Dave Vellante joins me, I'm John Walls here at theCUBE, coming to you live from our nation's capital. Joined by Team AWS here. With us we have rather, Ray Zhu rather, who is a senior product manager at AWS. And Roger Barga, who is the general manager of Amazon Kinesis Services. So gentlemen, thanks for being with us, we appreciate the time. >> Absolutely, thank you for the invitation. >> Dave: Oh, you're welcome. >> You bet. Alright, so let's just jump in. The streaming data thing, right? It's just blowing up. What's inspiring that popularity of the Cloud? What's kind of lit that fire and what's going to keep it burning? >> Yeah, I think over time, I think customers really do realize the value that you can get out of by collecting, analyzing, and reacting to data in real time. Cause that really provides a very differentiated experience to their customers, you know, for example you're able to analyze your user behavior data in real time, provide them with a much more engaging experience, much more relevant content. You're able to diagnosis your service, understand your law of data issues in real time, so that when you have an issue, you can fix that right away. So that really provides a very different customer experience. So I think our customers are realizing the value of real time processing, which is why we think streaming data is gaining more and more popularity. And this is why Cloud is all the good stuff that Cloud can offer and tell the customers. It's highly scalable, so you don't need to worry about if it's going to scale later on when I scale my business. It's a matter of sort of like click of a button. We scale the infrastructure for you and we got all the resource ready for you to go on streaming data. We got super, it's very cost effective, right? So that cause we price at very low. As we keep improving the efficiency of running the service, we reduce our cost structure, we return that back to our customers as a price cut. The third thing which I think is super important is agility, right cause you don't need to set up an infrastructure, install any software, make all the configurations. Starting up a Kinesis Stream is like 15 seconds on the average console, you're done. And it really allows the developers, the customers, to move fast and purely focus their resources and effort on the things that really differentiate their customer experience. >> So very AWS like, we love AWS, we're a customer, it's our favorite Cloud. We'll go on record of saying that, you know? (laughs) We're loyal to you guys. Crowd, our Crowd Chat App runs on it, basically run our whole company on Amazon, where we can. >> Roger: Great. >> In 2013, we got the preview of Kinesis. It was a lot of buzz. It was kind of before the whole streaming meme took over. We were talkin' about real time at the time, but so maybe you can take us through the evolution of Kinesis and where we are today. >> I'd be happy to. You know, when we first built Kinesis Stream, what the company was trying to do, is we had all of the AWS billing and metering records coming from all of our services, our EC2 incidences. This was a lot of data that had to be captured. And the way we were doing it was in batch. We were storing this data in S3 buckets. We were starting large EMR jobs up at the end of day actually to aggregate them by the customer account. So say this was your bill for the end of the day. But we had customers that said actually I'd like to know what I'm spending every hour, every few minutes. And frankly that batch processing wasn't scaling. So we had to innovate and create Kinesis Streams as a real time system that was constantly aggregating all of the billing and metering records that were coming in from our customer's accounts. Totalling them in near real time and we presented our customers with a new experience of billing and insights into their billing and even forecasts of what they were spending at any given time. But we had other teams that immediately looked at Kinesis and said hey, we're dealing with real time streaming data and our customers want it delivered and aggregated and provided, so Cloud watch logs and Cloud watch metrics built on top of us. And this was the start of something which continues to this day. Other services are looking at, and even customers, are looking at a Kinesis Stream and saying, that's a really useful abstraction that we can build a new service, a new experience for our customers. And today we have over a dozen AWS and Amazon retail services that build on top of Kinesis Streams as a fundamental abstraction to offer new experiences and new insights as three events. Cloud watch events, there's a host of services, which underneath Kinesis is running, but they're offering unique value building on top of it. Which is why Kinesis today is considered a foundational service and we can't build an AWS region without Kinesis being there for all these other services to build on top of. So that's been exciting to see that kind of adoption, different uses for this fundamental abstraction called a Kinesis Stream. And you know, it's also, and we can talk later about how it's transforming analytics, which is really exciting as well. >> Well, that's a great topic. I mean, why don't we talk about that. And one of the things that we've noted about AWS, and other Cloud providers, is obviously simplicity and delivering as a service is critical. We all know about the complexity of, for instance, the Hadoop Ecosystem And the challenges that a lot of customers have. Delivering that as a service has dramatically simplified their lives. That's why you see so many people going to the Cloud. We've always predicted that is what happened. Maybe talk about that a little bit. And then we can get into the analytics discussion. >> Yeah, so again, customers are always looking at ways to actually get insights into their data to better support their customers, to better understand what's going on in their business. And of course, Hadoop had managed EMR, had been a great benefit, cause customers could move their developers into the analytics that they want to do and not worry about this undifferentiated heavy lifting of operating these services. And the same is true for Kinesis Streams. But we're seeing customers, and if you stop for a moment and think about this, data never loses it's value. It always has it's historical value for machine learning, for understanding trends over time, but the insights that data has are actually very, very perishable and they can actually turn to zero within an hour if you can't extract those insights. That's the unique area where Kinesis Streams has kept adding value to our customers. Giving 'em the ability to get instant insights into what's going on in their business, their customers, their business processes, so they can take action and improve a customer experience, or capitalize on an opportunity. So what we're seeing and the role, I believe, that streaming data, at large, plays is about giving customers real time insights and then business opportunity to improve how they run their business. >> So. >> Go ahead, please. So who's using it? I mean or what's the if there's a sweet spot or a sweet spot for an industry or vertical to use that, I mean, in terms of whether it's in a minute, an hour, or whatever, what would that be? >> Yeah, so today, I'm really pleased to see, because we have watched this evolution since 2014, but today in virtually every market segment, where data is being continuously generated, we have customers that are actually taking advantage of the real time insights that they can get out of that data virtually every market segment. I'll pick a couple of examples which are kind of fun. One is Amazon Game Studios, near and dear to our heart. Now typically games are written, they're completely developed end to end. They're shipped in a box, made available to customers, and they hope that game and the engagement has the outcome that they want. Amazon Games Studios is actually writing that game in near real time ahead of their customers, so they release a new level of the game. They will actually watch the engagement. They'll look at how customers are dying, surviving, how long they're playing. And is it traveling in the direction they want? They stream all of the multi, all of the game data from their players in real time. And they build dashboards so they can see exactly how game play is going. And if they don't like it or they think they can make an improvement, they'll get right online, change the game itself, and re-deploy the game, so the customer experience is actually, within minutes it's being evolved. Another customer I like to talk about is Hertz Publishing. We all like to read. When Hertz started making the transition of their magazines, Cosmopolitan, Car and Driver, from print to digital form, they instrumented it so they could actually watch how long was a customer reading an article, how were their comments trending in Twitter and in Facebook. So they could actually get a sense of engagement with an article. Whether the article should be rebroadcast to other digital channels, other magazines. Should they change the article? Double down and write a new one. So again, they're engagement and then the business metrics by which they measure engagement and readers, readership have all increased because they have that intimate understanding of what's happening in real time. So again, every market segment, where there's data continuously generated, customers are using this to provide a better experience. >> That phrase undifferentiated heavy lifting we first heard it widely in the tech community in 2012 in Andy Jassy's keynote at Reinvent and it's become sort of a mantra. It probably was one well before that inside of AWS. And often times AWS doesn't talk about TCL but it's not the main reason why people go to the Cloud. You emphasized that a lot. And there's all this debate. Oh a cheaper on prem, oh no, Cloud is cheaper. But this idea of essentially eliminating labor that is doing that non-differentiated heavy lifting is something that you guys have really lived and popularized. We see that labor cost shifting from provisioning luns into other areas, up the stack, if you will. Application, digital business, analytics, et cetera. What are you guys seeing, in terms of how organizations, I mean, there's two types of organizations, right, the Cloud native guys who obviously didn't have the resources, but then enterprises that are bringing their business to the Cloud. Where are they shifting that undifferentiated heavy lifting labor towards? >> To. And they are in fact moving it up stream. We think about it very abstractly. You know, operating servers doesn't really bring any special IP that that company possesses to bear. It is about, you know, just managing servers, managing the software on it, figuring our how to scale. These are problems which we are able to take away. And we've often worked with customers and showed them the value of moving to our managed servers. And the excitement from the leadership, from their customers, is like wonderful. That project we couldn't, we aren't able to fund, if we can just onboard here, onto Kinesis for example, or any one of our managed services, then we can immediately move and get that fund project that we really wanted to fund, it would actually be unique value as move them over to that. So they're actually moving upstream as you said. And they're actually leveraging their unique understanding of their industry, their customer, to go ahead and add value there. So it is a distribution and I think in a very productive way. >> I want to ask about the data pipeline. So one of the values that AWS brings is simplification. When I look, however, at the data pipeline, it's very rich. If I look at the number of data services, Kinesis, Aurora, DYNAMO dv, EBS, S3, Glacier, each of these has a programming interface that is, I use the word primitive not in pejorative way but >> Roger: Yes, yes. >> But a deep level, low level. And so the data pipeline gets increasingly complex. There's probably a benefit of that, because I get access to the primitives, but it increases complexity. First of all, is that a fair assertion on my part? And how are your customers dealing with that? >> Be happy to take that one, yeah? >> Sure. >> Okay. >> Yep, so I think from our perspective all these different capabilities and technologies by customer choice. We build these services because our customers ask for them. And we order a wide variety so that people can choose for the developers who want to have full control over the entire staff, they have access to these lower level services. You know as you mentioned a few, DYNAMO dv, Kinesis Stream, S3, but we also build an abstraction layer on top of these different services. We also have a different set of customers asking for simplicity, just doing a specific type of things. I want you guys to take care of all the complexities, I just want that functionality. The example would be services like Kinesis Files, Kinesis Analytics, which is the abstraction layer we put on top. So for customers who are looking for simplicity, we also have these kind of capability for them. So I think at the end of the day, it's customer choice and demand. That's why we have this rich functionality and capabilities at AWS. >> So you guys have already solved that problem essentially, the one that I was sort of putting forth. >> So I won't say, I like Ray's answer. It's about listening to the customer. Cause in many cases if we would have, if we said, hey, we're going to go build a monolithic service that simplifies this, we would potentially disappoint many other customers. Say actually I really do want to have that low level control. >> Right. >> I'm used to having that. But when we hear customers asking for something which we can then translate to a service, we'll build a new service. And we will actually up level it and actually build a simpler abstraction for a targeted audience. So for us it's all about listening to the customers, build what they want, and if it means that we're going to actually bring two or three of our services together to work in concert for our customer, we'd do that in a heartbeat. >> Yeah that low level control also allows you to be presumably maybe not more agile but more responsive to the market demand. Because if you did build that monolithic service, you would essentially be locking yourselves in to a fossilized set of functions and services that you can't easily respond to market conditions. Is that a fair way to think about it? >> That is a fair statement, because basically our customers can look at these API's and together for these various services, realize how to use these API's in concert to get an end and done. And should we have precise feedback on a specific service, we can add a new API or tailor it over time. So it does give us a great deal of agility in working on these individual services. >> So Ray, you're a product guy and you're talking about listening to customers, right? And coming up with products, it's what you do. What are you hearing now? Where do people want to go now? Because I assume you've been in the market place for four years now with this, evolution is (clears throat), excuse me, perpetual, constant, so where do you want to take it? What's the next level or what's percolating in the back of your mind right now? >> Yeah, I think people always looking for different type of tools that they're familiar with or they want to use to analyze these data in real time and provide a differentiated customer experience. A concrete example I want to give is actually why we're here. At the Splunk Conference is at Kinesis we have a service called Kinesis Firehose. Based on customer demand when we launched Kinesis Streams, customers wanted to make sure they had access to data sooner than they used to do, but they want to use the tools they're familiar with. And apparently there's a diverse set of tools different customers want to use. We started with S3 for data lay, kind of storage, we used Reshift as a data warehouse. And overtime we heard from customers say, hey, we want you to use Splunk analyze the data. But we would like to use Kinesis Firehose and suggest a solution. Can you guys do something about it? So actually the two teams got together. We thought it's a strong customer value proposition, great capability for other customers. So we start this partnership. We're here actually earlier this day, today, we made the announcement actually, Kinesis Firehose is going to support Splunk as data of redestinations. And this integration is not in beta program. It's open for public sign up. Just go to the Kinesis Files website. You can sign up, get early access. So basically from today, you can use Kinesis Firehose in real time streaming (mumbles) service to get real data into your Splunk cluster. We're super excited about it. >> And okay, and I can access those Splunk services through the market place or what's the way in which I bring Splunk to? >> Good question. For this integration actually we're just a different version of Splunk. You can run Splunk on AWS using ECT extensions. You can access through the market place. You can have your, you can use native Splunk Cloud, which manage all the servers for you. You can also use Splunk on print in that regard. >> Okay. What have you guys learned since the orig, the first reinvent? I mean, I think, and again, I don't mean this as a pejorative but AWS is pretty dogmatic in its view of the world as you you are very strict (laughs) about your philosophy. But at the same time, as you learn about the enterprise, you've evolved. What have you learned about enterprise customers in that five, seven year journey of really getting intense with the enterprise? >> Yeah, that's a good question. But again, we're dogmatic about we always listen to our customers. We will never deviate from that. It's part of our culture. And the customers need to tell us where they want to go. And I'll tell you when we first started with Kinesis, just to answer your question, it was about low latency. We want to get that answer really fast, cause our ad tech customers are some of our very early customers, so it really was about that that extremely low latency response. As even our customers have started to look at Kinesis as a fundamental abstraction on which to put all of their business data in and now they're telling their customers well you should, if their IT customers within their company, if you want any business data, attach to the stream and pull it out. So now we're seeing less emphasis on low latency and to end processing, but increase request I want to be able to attach a dozen consumers, because this stream is actually supporting my entire enterprise. I want to have security. So we recently released encryption at rest. Our customers are asking for support for a VPC flow logs, which we hope to be talking with you about very soon. So now it's becoming actually very mainstream to actually, for the enterprise, and they want all the enterprise ready features, all the certifications, Fed Rep, Hippa, et cetera. So now we're actually seeing the Kinesis Stream itself being put into the enterprise as a fundamental building block for how they're going to run their business and how they're going to build their applications within the business. >> So that philosophy, I mean, you are customer driven first and there's a lot a, Andy Jassy says, there's a lot of ways to compete. You can be competitive oriented, but we're customer oriented. And I, it's clear, you guys do that. At the same time, customers sometimes don't know what they want, so you have to be good at decoding. >> Roger: Yes. >> If you listen to all your customers, you know, five years ago, they say, well we're not going to put any data in there. Sensitive data in the Cloud. Now everybody has sort of gotten over that. You said, alright, well we have to make it more secure. We have to get, you know, whatever certified, et cetera, et cetera. There's an art to this, listening to customers, isn't there? >> It gets back to one of our leadership principles of we always work customer backwards. We need to understand what they want, what experience they'd like to have. We have to anchor everything on that. But there is this element of invent and simplify. Because our customers may guess at what a solution is, but let's make sure we really understand what they want, what they need, the constraints under which that solution must offer. Then we go back to our engineering teams and other teams and we invent and simplify on their behalf. And we're not done there. We actually then bring these back to customers and in fact, why we're here today, we've spent two days talking to customers but even before this collaboration with Splunk began, we actually brought customers in and it turned out, their customers were often our customers. So we started talking, what is the problem? And we started with the very clear problem stain. And once both of our teams, we've loved working with Splunk, they work very customer backwards, like we do. And together once we understood this is the problem we are trying to address, and we had no preconception about how we're going to do it, but we worked backwards on what it would take to actually get that experience for our customers. And we're actually here beta testing it. And we're going to have a very aggressive two or three month beta test with customers, did we get it right? And we'll refine as well before we actually release it to the customer. So again, that working with the customer, work customer backwards. But invent and simplify on their behalf. Because many Splunk customers weren't aware of Firehose until we explained it to them as a potential solution. They're like ah, that will do it, thank you. >> So very outcome driven. I mean, I know you guys write press releases before you sometimes launch products. Sort of as you say, that's what you mean by working backwards, right? >> Roger: Yes, yes it is. It really is. >> Ray: You're good listeners. >> So far it's worked. (laughter) >> It's always fun at the company, when somebody says I have a customer, the entire room gets quiet and we all start listening. It's actually fun to see that, because that's the magic word. I have a customer and we all want to listen. What do they want? What are they challenged with? Cause that's where the innovation starts from which is exciting to be part of that. >> It's been a great formula, no doubt about that. >> It has, it has. >> Thank you both for being here. Didn't realize it was a big day. So congratulations >> Thank you. >> on your announcement as well. >> Absolutely. >> Ray, Roger, good to see you. >> It's great talking with you. >> Alright, you're watching theCUBE live here from Washington D.C. .conf2017. (techno music)

from Las Vegas accepting the signal from the noise it's the kue coverage interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back around we are here live in Las Vegas for IBM interconnect 2016 special presentation of the cube our flagship program would go out to the events and extract the signal from the noise I'm John forreal echoes gave a lot they are next guest pricing Kohler who's the chief information technology officer and I'm saying this for the first time on the cube the weather company and IBM business welcome back to the cube thank you very much glad to be back last time you weren't an IBM business we were just the weather company were just the weather company so congratulations on your success want to say we really big fans of it but what Papa Chiana the team have done is visionary bold and very relevant so congratulations hey how's it feel it is grateful din we are really excited the opportunity with the IBM platform and you know the reach and the capabilities I mean it it really helps accelerate what we were trying to get done as the weather company you know as our own standalone business um and you know as you try to prepare and protect the entire planet all of its people and all of its businesses prepare and protect them for tomorrow which is really what the weather is company is all about finding that intersection of consumer behavior helping prepare and protect you as a in your personal life and your family but also you as a business owner how do we prepare and protect you to do better tomorrow because of the weather and the insights that we can provide fit straight into the work the Bob picciano in team have been doing with the insights you know economy with Watson and analytics with insights as a service all of that just kind of plugs together in it it really is a natural fit it's interesting to see IBM's move we were asked to guess on from IBM earlier and Jamie Thomas said it's all open source we want to get in early so this is an early bet for IBM certainly a bold move with the weather company but it's interesting the scuttlebutt as we talk to our sources inside the company close to the company have telling us that the weather companies is infiltrating and affecting the DNA IBM in a good way and you guys have always been a large scale data company and that is what all businesses are striving to digitize everything yes and so take us through that I mean one I think it's fair to say that you guys are kind of infecting I play in a positive way the mindset of being large-scale data yeah well why is that so compelling and how did you guys get here obviously whether the big data problem share some commentary around where it all came from well i think you know it's in my DNA first of all and it's in our company's DNA it's are no teams DNA you know I'm a change agent you would not want to hire me to maintain something good if you want to hire me to you know to break something and rebuild it better that's I'm your guy so you know I think when you look at the movement from you know the kind of the movement over time of IBM and you know the constant evolution that IBM goes through time is ripe when you take the cloud capabilities and you take data and you take analytics and the whole concept and capabilities of Watson Watson gets smarter as it learns more Watson can only be as smart as the data you feed it and so for Watson to continue to learn and continue to solve new problems and continue to expand its capability set we do have to feed it more data and and so you know looking at whether whether it was the original big data problem ever since the first mainframe the first you know application ever written on a mainframe was a weather forecast and ever since then everybody's been trying to figure out how to make the forecast more accurate and a lot of that comes from more data the more data you have the more accurate your forecast is going to be so we've been trying to solve this big data problem Walt and Dave talks about it was saw earlier in the opening about digital assets and in this digital transformation companies have to create more digital assets that's just dating yeah in this new model so when you look at the data aspect you say whether also is a use case where people are familiar with we were talking before we went on camera that people can understand the geekiness of whether it's different they're familiar with it but also highlights a real-life use case and the IOT Internet of Things wearables we heard you have sports guys on here tracking sensors this brings up that digital digitizing is going to be everything not just IT right it makes it real right if I think about my parents right we've been talking about IOT hey dad you're gonna have a connected refrigerator why does he care what do I need a connected refrigerator for but as you start to bring these insights to life and you make them real and you say you know what if I actually understand the humidity levels in your house and I can get that off the sensor on the air intake of your refrigerator I can now correlate that the humidity level outside of your house and I might be able to actually tweak your HVAC and I can make that run efficiently and I can now you know cut thirty percent of your cooling costs and all of these you know examples they're integrated they become real yeah and and I think weather is great because everybody checks their weather app the weather channel app or the weather underground app every day they're always looking at it and you know we get it right seventy-eight percent of the time we'd get it wrong sometimes we're constantly working to maintain our number-one position and data accuracy on weather forecasting and you know the more data we have the more accurate we can make it and so we've got any safer to you think just think about the use cases of people's lives slippery rose you know events correct I mean it's all tied in no goes back to another you know if I understand what's going on with the anti-lock braking system of a car and I already have a communication vehicle into everybody in that car which is our appt in their pocket I can alert them if the car is up ahead are having here are their abs activated and if all of the cars up ahead are having their abs activated I could alert them two miles back and say hey get ready slow down it's real it's not forecasted it's real data I'm giving you a real alert you should really take action and you know as we move from you know weather-alerts that we're looking out forward in time many hours as we're now doing rain alerts where we tell you it's going to start raining in the next seven minutes ten minutes people love those because it's right now and I can make a decision right now lightning strikes are always fascinating oh god because I gotta see crisis so last fall at IBM insight we interviewed David Kinney death your CEO and then right after I think was the week after I was watching some you know I was in Boston watching some sports program and there's bill belichick complaining about the in accuracy of whether i'll try that whether some reporter asked him about you know you factor in the weather i don't even pay attention i look at the weather forecast they're always wrong as a wait a minute I just I just interviewed David Kennedy he was bragging on the weather is the accuracy and how much it's improved so helping you mentioned seventy-eight percent of the time it's it's gotten better over time it has it still got rooms we're not perfect so so talk about that progression it is the data but how much better are you over time where is that better is it just short term or is it longer term at so color to that it's a great question and it's a fair point I think one of the biggest changes we've made in the last three years that the weather company is we've taken our forecast from what was roughly 2 million locations where we would do a forecast two million locations around the globe and today we we create a forecast for 2.2 billion locations around the globe because the weather is different at Fenway then Boston Logan it's just different than the the start time of rain the start time of a thunderstorm you know that's gonna be different now maybe five minutes but it's different the temperature the wind it's different and so as we've increased the accuracy and granularity of ours are our locations we've also done that from a time perspective as well so we used to produce a forecast every four to six hours depending upon how fast the models ran and did they run and complete successfully we now update our forecast every 15 minutes and so we we've increased the the you know all aspects of that and when you when you now think about getting your weather forecast you can no longer just type in BOS for your airport code and say i want to know what the weather is at boston logan if you're you know if you're in cambridge the boston logan forecast is not accurate for you you know five years ago every that was fine for everybody right right and so we have to retrain people to think about and make sure that when they're looking for a forecast and they're using our apps they can get a very specific forecast for where they are whatever point on the globe they are and and don't have you know Boston you know Logan as your you know favorite for your city if you're sitting in Cambridge or your you know you know it in Andover further outside where I am now where you gonna be my guess I gotta get so different you leverage the gps capabilities get that pinpoint location it will improve what the forecast is telling so I feel like this is one of those omni headed acquisition monsters for lack of a better term because when the acquisition was first announced is huh wow really interesting remember my line Dell's by an emc IBM is buying the weather company oh how intriguing it's a contrast it's all about the data the Dane is a service and then somebody whispered in my ear well you know there's like 800 Rockstar data scientists that come along with that act like wow it's all about the data scientists and then on IBM's earnings call i hear the weather company will provide the basis for our IOT platform like okay there's another one so we're take uh uh well i think IBM made a very smart move i'm slightly biased on that opinion but I think I be made a very smart move at very forward-looking move and one built on a cloud foundation not kind of a legacy foundation and when you think about IOT data sets we ingest 100 terabytes of data a day i ingest 62 different types of data at the weather company i ingest this data and then i distributed it massive volumes so what we had fundamentally built was the world's you know largest cloud-based iot data platform and you know IBM has many capabilities of their own and as we bring these things together and create a true next-gen cloud-based IOT data engine the ability for IBM to become smarter for Watson to become smarter than all of IBM's customers and clients to to become smarter with better applications better alerts better triggers and that alerts if you think about alerting my capability to alert hundreds of millions of people weather-alerts whether that's a lightning alert a rain alert a tornado warning whatever it is that's not really any different than me being able to alert a store clerk a night stock clerk at the local you know warehouse club that they need a stock you know aisle three differently put a different in cap on because we now have a new insight we have a new insight for what demand is going to be tomorrow and how do we shift what's going on that alert going down to a handheld device on the guy driving the four club yeah it's no different skoda tato yeah the capability to ingest transform store do analytics lon provide alerting on and then distribute data at massive scale that's what we do we talk about is what happened when Home Depot gets a big truck comes in a bunch of fans and say we know where this know the weather company did for you yeah we don't understand you'll understand you'll fake it later they file a big on the top of it so I OT as well as markets where people don't can't understand that some people don't know it means being like what's IOT Internet of Things I don't get it explain to them some little use cases that you guys are involved in today and some of these new areas that you're highlighting with with learning somehow see real life examples for for businesses and users there is a smarter planet kind of you know safe society kind of angle to it but it's also there's a nuts-and-bolts kind of practical if business value saving money saving lives changing you know maintenance what are some of the things share the IOT so there's there's only two things there so one is what is IOT and IOT really is is sensor data at the end of the day computers sensors electronic equipment has a sensor in it usually that sensor is there to do its job it's there to make a decision for what if it's a thermostat it has a sensor in it what's the temperature you know and so there are sensors in everything today things have become digitized and so those sensors are there as next as those next evolutions have come online those those sensors got connected to the Internet why because it was easier than to manage and monitor you know you know here we are at the mandalay bay how many thermostat sensors do you think this hotel casino complex has thousands and so you can't walk around and look at each one to understand well how's the temperature doing they all needed to be shipped back to a central room so that the in a building manager could actually do his job more efficiently those things then got connected so you could look at it on a smartphone those things they continued to get connected to make those jobs easier that first version of all of those things it was siloed that data SAT within just this hotel but now as we move forward we have the ability to take that data and merge it with other data sets there's actually a personal a Weather Underground personal weather station on the roof of the Mandalay Bay and it's actually collecting weather data every three seconds sending it back to us we have a very accurate understanding of the state of the Earth's atmosphere right atop this building having those throws is very good for the weather data but now how does the weather data impact a business that cares about the weather that has there we understand what the Sun load is on the top of this building and so we can go ahead and pre-heat your pre cool rooms get ahead of what's changing out sign that will have an impact here inside we have sensors on aircraft today that are collecting telemetry from aircraft turbulence data that helps us understand exactly what's going on with that airplane and as that's fed in real-time back down to the earth we process that and then send it back to the plane behind it and let that plane behind it know that it needs to alter it course change its flight plan automatically and update the pilots that they need to change course to a smoother altitude so gone are the days of the pilot having to radio down and fall around his body it's bumpy to get these through there anywhere machines can can can do this in real time collected and synthesize it from hundreds of aircraft that have been flying in that same route now we can actually take that and produce a better you know in flight plan for those for those machines we do that with with advertising so you know when you think about advertising you be easy the easy example is hey we know that you're going to sell more of X product when y weather condition happens that's easy but what if I also help you know when not to run an ad how do I help save you money you know if I know that there's no way for me to actually impact demand of your product up or down because we know over the course of time looking at your skew data and weather data that no matter what what we do weathers gonna have this impact on your product save your money don't run an ad tomorrow because it doesn't matter what you do you're not going to actually move your product more that's great and it's much business intelligence it's all the above its contextual data help people get insights in subjective and prescriptive analytics all rolled into one in a tool that alerts the actual person may explain to people out they were predictive versus prescriptive means a lot people get those confused what's your how would you prescriptive is you know where we want data that just tell us what to do based upon historic looking trends so i can take ten years of weather data and I can marry that up with ten years of some other data set and I can come up with you know a trend based upon the past and with that then I could prescribe what you should do in the future hey looks like general trend bring an umbrella tomorrow it's good it might rain but if I get into predictive analytics now I can start to understand by looking at forward-looking data things that haven't happened yet or new data sets that I'm merging in in real time oh wait a minute we thought that every time it rained more people went to this gas station to fill up but wait a minute today there's an accident on the road and people no matter what we do they're not going to go to that gas station because they're not even going to drive by it so being able to predict based upon feet of our real-time data but also forward-looking data the predictive analytics is really around the insights that we want to guess I got to ask you one question about the IBM situation and I want you to kind of reflect get him get you know all right philosophical for a second what's the learning that you've had over the past few weeks months post-acquisition inside IBM is there a learning that you to kind of hit you that you didn't expect there's something you'd expect what sure what was your big takeaway from this experience personally and you had some great success in the business now integrated into IBM what's the learning that cuz that's comes out of this for you I am really proud of the team at the weather company you know I I think what we have been able to accomplish as a small company you know comparative to my four hundred and sixty-eight thousand colleagues at IBM yeah what we've been able to accomplish what we've been able to do is really you know it's impressive and I've been proud of my team I'm proud of our company I'm proud of what we were able to get done as a company and you know the reflection really is as you bring that into IBM how do you make sure that you can you can now scale that to benefit such a large organization and and so while we were great at doing it for ourselves and we built an amazing business with amazing growth you know attracted lots of people that looked at buying us and obviously IBM executing on that I think that's amazing and I'm proud of that but I think my biggest reflection is that doesn't necessarily equate to success at IBM and we now have to retool and retrans form ourselves again to be able to take what we know how to do really well which is build great capabilities build big data platforms build analytics engines and inside engines and then armed a sea of developers to use our API we can't just take what we've done and go mate rest on your laurels you gotta go reinvent so I think my biggest you know real learning and take away from the kind of integration process is well we have a lot to learn and we have a lot of change we need to do so that we can actually now adapt and and continue to be us but do it in a way that works as an IBM ER and and that's that's there's there's going to be an art to this and we've got a ways to learn so I'm going in while eyes wide open around what I have to learn but I also am very reflective on on how proud I am as a leader of the team that you know has created you know such an amazing capability acquisition is done you savor it you come in you get blue washed and I hope I had a Saturday afternoon where I say okay got all like what is this gonna think so and then okay so you you wake up in the morning and you sort of described at a high level you know what you're doing but top three things that you're focused on the next you know 12 12 months so so you know the biggest thing that I'm focused on number one is making sure that we protect the weather company culture and how we know how to do and build great things and so I've got to lead us through obviously becoming integrated with IBM but not losing who we are and IBM is very supportive of that you know Bob picciano his team have been awesome and you know John Kelly and team have been awesome everybody that we have worked with has been so supportive of Bryson please make sure you find the right way through this we don't want to break you and I think that's natural for any acquisition for any yeah but you guys aren't dogmatic you were very candid saying we're gonna transform ourselves and adapt absolutely and so and so so we've got that on wrestling on my mind how do we go find immediate wins there's there's a a million different ways for us to win there's thousands of IBM sales teams that are out in front of clients it's just today with new problems how do we quickly adapt what we've been good at doing and help solve new problems very quickly so that's on my mind and then you know wrapping that in a way that becomes self service we can't I don't want to scale my team through people to solve all these problems I want to find a way to make sure that all these capabilities new data sets new insights new capabilities that we bring the life I want to do that in a self-service way I want to make sure that our technology the way we interact with developers the developer community that we bring in to kind of work on our behalf to make this happen I don't want to solve all these problems I want to enable others to solve the problems and so we're very focused on the self service aspect which i think is very new prices thank you so much taking the time out of your busy schedule to see with us in the queue good to see you again or any congratulations IOT everything's a sensor that we're a sense are here in the cube and we sense that it's time to go to SiliconANGLE DV and check out all the videos we have a purpose our sensor is to get the data to share that out with you thanks for the commentary and insight appreciate it whether company great success weather effects of song could affect stock prices all kinds of things in the real world so we had a lot of a lot of big data thank you very much look you here live in Las Vegas right back more coverage at this short break

>>Live from Las Vegas, Nevada. It's the cube covering EMC world 2015. Brought to you by EMC, Brocade and VCE. >>Okay. Welcome back everyone. We are live here in Las Vegas with the cube at EMC real 2015. I'm John ferry, the founder of Silicon Ang. I'm joined with two special guests. Stella Lowe. Who's the global communications at EMC runs, global communications and Amy Posey, neuro facilitator at peak teams. Welcome to the cube. >>So >>You had a session women of the world. We did it last year, but great cube session last year. Um, so I want to get a couple of quick questions. What's going on with women of the world, what you guys just came from there and you guys were on the panel and then what is a neuro facilitator? And then let's get into it. Let's talk about men and women, how we work together. >>Okay, great. So let's start with women of world. So, um, so last year we talked about the challenges that we face and how we reframe them into opportunities that we had some fantastic panelists, but this year I was really interested in the science behind men and women. So it's clear that we're different and we're all bled for success, but, but we're wired differently. And we kind of knew that already. I know we talked about it before John, but we now have the science behind it. We can look at brain scans and we can see that we, Oh, we have different brain patterns. We think differently, uh, different parts of the brain fire fire up when, in times of motivation and stress and people like Amy here, who've done lots of work into this, have having the stages. It was great to have her on the panel to discuss it. >>I'm going to give you a plug because EMC does all kinds of things with formula one cars, motorcycles, getting the data and understanding the race. But now you're dealing with people. So what is going on? Tell us what's up neuro facilitator and let's >>So a neuro facilitator is maybe the best made up job title in the world that I gave myself. So essentially what I do is I look at information about the brain and I curate the research that's out there. So there's a lot of new technology to actually read and look inside our heads. We all have a brain, but we don't necessarily all know how it works. So there's a lot more research and, and tools to read our brains and take a look inside. So what I do is I take that research and, and work with, um, neuroscientists and neurobiologist at Stanford, Columbia, UCLA, and, and reach out and figure out how do we take that information and make it easier, still attain. And I do it in the scope of leadership at organizations like EMC and other technology companies to figure out how do we work better? What information is out there? You know, soft skills and sort of relationship skills. I've always been sort of squishy, right? So now there's a lot more science and information about our brains that are informing it. The, the data's out there, what I do and what my job is, is to pull the data and figure out how do we make it into practical, useful applications for us at work at home, wherever we are. So that's essentially, I'm doing so you >>Guys discussed and how men and women are different. Actually look at the data. We have to give a lot of qualitative data. I mean, it keeps counselors in business. You know, the grant in the workforce, uh, balance is important, but we have a lot of that data, but what's the numbers. What is your findings? So >>What's interesting is looking at men and women's brains. What's fascinating is that we are more alike than dissimilar in looking at a brain. If you looked at a brain scan, one of a man and woman, you wouldn't be able to tell the difference between the two, but they're now finding and looking at different parts of the brain in different functions. So for instance, men have approximately 6% more gray matter than women. So in terms of the gray matter, that's the thinking brain essentially, and women have more white matter than gray. Matter about 9% more than men. And the white matter is what connects the brain and communicate both front and back and side to side. And so you can make some extrapolation of that information and say, you know, men may focus more on issues, solutions, problems, whereas women sort of think more broadly or wider. >>So, I mean, there are generalities, but a lot of the sciences is fascinating. There's also some interesting science about the hippocampus, which is, um, sort of deep. If this is your brain, it's deep inside the brain and the hippocampus is the memory center. And it's what they're finding is that for women, they tend to store emotional memories more effectively. So happy, sad, fearful those types of emotions get stored more effectively in the hippocampus. Whereas men oftentimes during stress, the hippocampus actually has a challenge in making connections. So that's where, again, some of the, the focus and determination and silo viewed sometimes that men have in situations or problems comes into play. Um, there's one other piece, the anterior cingulate cortex, which is sort of within the brain and that's the brains error detector. And it turns out it's a little bit bigger in women. So women sort of tend to look for, uh, issues CA you know, problems, um, maybe less solution focused, especially under times of stress and, and a lot of this, data's interesting. >>It, it causes you to make some generalities, you know, not everybody is going to operate in that way. Your mileage may theory, but it's, it's good because it helps us inform some of the quirky behavior that we deal with at work and figuring out why, why don't you do that? Why do you do that and installed that women being better or women using more of the brain or less of the brain it's, it's, it's simply about we, we, if all brains away from differently, we both bring different things to the table. And how do you take both of those benefits and bring them forward into a better outcomes? >>Always great to talk about because in the workforce, people are different. And so differences is a term that we use, like, you know, with kids learn differently, some have evolved differently and men and women have had differences. So the data shows that that's clear. Um, I want to share a quote that my wife shared on Facebook. It says mother, um, well, a worried mother does better research than the FBI. So, um, I bring that up, you know, it's instinctual. So a lot of it's also biological and also environmental talk about the dynamics around that, that wiring, because you're wired by your upbringing too, that affects you. And what's the, what's the data show in the biology. >>So it's interesting because the, the key piece is that it's not just the biological brain differences. It's, it's a whole host of factors that leave a footprint on us, in our behavior. So it's our education, it's our, uh, you know, where we, where we grew up, our culture is part of that. It's also gender stereotypes that play a role in how we operate. And I think all of those things leave a footprint on a, an and lead us to different behaviors. And so you can't just say it's the, the, the information that's on our brains. It's a whole host of factors that influence. So my study of looking at how the brains are a little bit different and what the research is coming, it's, it's blended in with research around leadership and things like confidence and motivation in the workplace bias in the workplace. And they're, they're showing very different things. >>So for instance, if you think about confidence, we did an interesting exercise in the event at women of world. And I asked, you know, there's, there's a lot about confidence and confidence is essentially the will or motivation to act. So how many women in the room, uh, would raise the, you know, go up for a job that they were really interested in and fascinated by, but maybe weren't a hundred percent qualified for, like, how many of you have maybe turned down that job or decided not to apply because it wasn't the right time. Like you, you're pretty competent, but not a hundred percent confident in it. And it was funny because the majority, all the women's hands went up in the room. So then I asked him, I flipped the question in the room and I asked the men in the room. I said, okay, if you were only about 50% confident for a job that you were going up for, would you, of course, right. Like, yes, I >>Fabricate some stuff on their resume and you make >>Them look bigger. So, exactly. So what's interesting is testosterone plays a role in confidence and motivation at work. And it turns out men have 10 times the amount of testosterone as women do. So part of that is that aggression, but we both have it, but that, that aggressive factor, that idea to go after something, to be more confident, um, women are behind the curve in that, from the research that I've seen. So it takes more effort to, to, to be able to have the confidence, to go for it and to sort of break down those barriers that exist for women to, to go after those jobs that they want, even if it's not a hundred percent. And so we did a, an exercise in boosting confidence in testosterone called power posing. And Amy Cuddy out of Harvard does a, a whole Ted talk on it, which is fascinating. >>But the idea is that you, you know, you, you put your chest back, you put your hands on your hips and it helps boost your testosterone up to about 20%. And it reduces cortisol, which is a stress hormone. So it's a, it's a quick way. You don't do it in front of people. You do it sort of on the sly or else you kind of, you don't look very nice to others, but you, you boost your confidence doing that. And it's just a small sort of brain hack that you can do to give yourself an upper hand, knowing that knowing the science behind it. So it's a behavior changing type of research that's coming out, which I think is really, >>That's really interesting, but now it translates into leadership and execution in the workforce. So people are different than men and women are different that changes the dynamic around what good is, because if your point about women not asking for that job or having confidence to the field, like I'm not going to go for it, like a man bravado, whatever testosterone that's what mean that that's the benchmark of what drive means. So this came up with Microsoft CEO at the Anita board conferences, which we had a cube there. And, and this is a big issue. So how do HR, how do the managers, how do people recognize the differences and what does the data show, and, and can you share your thoughts on that? >>Yeah, so I think a lot of it comes down to bias and bias is essentially a shortcut that we use in our brains to take less energy. And it's not a bad thing. It's, it's something we all do. And it's conscious and it's unconscious. So bias, I think is a key piece of that. And the research on bias is fascinating. It's very, it's, it's very popular topic these days, because I think being able to do a couple of things, be aware that there are hundreds of biases and they're both conscious and unconscious, uh, acknowledge that it exists, but not legitimize it not make that. Okay. The third piece is to, to counter it and, and being able to counter bias by making sure that people have opportunities. And even though you may have re removed hypothetical barriers explicitly stating that you want people, men, or women to apply for promotions, be this type of leader, not just assume that because there are no barriers that it's okay, but really be explicit in how you give people opportunities and let them know that they're out there. I think that's really key. >>Yeah. That brings up the point around work life balance, because, you know, I have a family of four, four kids it's stressful just in and of itself to have four kids, but then I go to the workforce and the same with women too. So there's also a home dynamic with leadership and biases and roles. Um, what's your take on any data on the how of that shifting persona realities, if you will, um, shapes the data. >>It's interesting because it's, it's something that we even talked about in the session that it's a struggle and, and, um, Bev career from Intel was talking about that. There's a period of time that actually is really tough to keep women in the workforce. And it's that time where you're growing your family, you're growing your career. And oftentimes things sort of struggle. And I, I read something recently around women in STEM careers, over a 10-year period, 42% of women drop out of the workforce in comparison to 17% of men. And so I think there's a lot, a ways to go in terms of being able to set up environments where working life is integrated, because it's, it's not even balanced anymore. It's integration. And how do you set up structures so that people can do that through how they work through how they connect with others. And, and to me, that's a big piece is how do you keep people in the workforce and still contributing in that critical point in time? And, you know, Intel hasn't figured it out. It's a tough challenge, >>Stamina. We're a big fans of women in tech, obviously because we love tech athletes. We'd love to promote people who are rock stars and technology, whether it's developers to leaders. And I also have a daughter, two daughters. And so two questions. One is women in tech, anything you could share that the data can talk to, to either inspire or give some insight and to, for the young women out there that might not have that cultural baggage, that my generation, at least our worse than older than me have from the previous bias. So motivating young daughters out there, and then how you deal with the career advice for existing women. >>So the motivating young women to get into tech, um, Bev shared a really absolutely fascinating statistic that between the ages of 12 and 18, it's incredibly important to have a male support model for young girls to get into STEM careers, that it was absolutely critical for their success. And it's funny because the question came up like, why can't that be a woman too? And what's interesting. And what we find is oftentimes we give men the short shrift when they try and support women, and we don't want to do that. We want to support men supporting women because when that happens, we all win. Um, and so I think that's a big piece of it is starting young and starting with male support as well as female support. So many women who, who cite men as, as he had mental was in that gray, you know, or in their daily life. And it's pretty important that they can feel that they can do that. >>And this goes back down the wiring data that you have the data on how we're were wired. It's okay, guys, to understand that it's not an apples to apples. So to speak, men are from Mars. Women are from beans, whatever that phrase is, but that's really what the data is. >>And being explicit to men to say, we want you to support women instead of having men take a back seat feeling like maybe this isn't my battle to fight. It's, it's really important to then encourage men to speak up to in those, those situations to, to think about sort of women in tech. One of, uh, a really interesting piece of research that I've seen is about team intelligence and what happens on teams and Anita Willy from Carnegie Mellon produced this really fascinating piece of research on the three things that a team needs to be more intelligent. It's not just getting the smartest people in the room with the highest IQ. That's a part of it. You want table stakes, you want to start with smart people, but she found that having women, more women on a team actually improved the team's overall intelligence, the collective intelligence and success of a team. So more women was the first one. The second was there's this ability and women tend to be better at it, but the ability to read someone's thoughts and emotions just by looking at their eyes. So it's called breeding in the mind's eye. So just taking a look and being able to sense behavior, um, and, and what someone's thinking and feeling, and then being able to adjust to that and pivot on that, not just focusing on the task at hand, but the cohesion of a team with that skill made a difference. >>It's like if it's a total team sport, now that's what you're saying in terms of how use sport analogy, but women now you see women's sports is booming. This brings up my, my, your, uh, awesome research that you just did for the folks out there. Stella was leading this information generation study and the diversity of use cases now with tech, which is why we love tech so much. It's not just the geeky programmer, traditional nail role. You mentioned team, you've got UX design. You have, um, real time agile. So you have more of a, whether it's a rowing analogy or whatever sport or music, collaboration, collaboration is key. And there's so many new disciplines. I mean, I'll share data that I have on the cube looking at all the six years and then even women and men, the pattern that's coming up is women love the visualization. It's weird. I don't know if that's just so it's in the data, but like data scientists that render into reporting and visualization, not like just making slides like in the data. Yeah. So, but they're not writing, maybe not Python code. So what do you guys see similar patterns in terms of, uh, information generation, it's sexy to have an iWatch. It's >>Cool. So like a cry from Intel on the panel, she gave a great statistic that actually, uh, it's more it's women that are more likely to make a decision on consumer tech than men. And yet a lot of the focus is about trying to build tech for men, uh, on the, you know, if consumer tech companies want to get this right, they need to start thinking about what are women looking for, uh, because, uh, they're the ones that are out there making these decisions, the majority of those decisions. >>Yeah. I mean, it's an old thing back in the day when I was in co, um, right out of college and doing my first startup was the wife test. Yeah. Everything goes by the wife because you want to have collaborative decision-making and that's kind of been seen as a negative bias or reinforcement bias, but I think what guys mean is like, they want to get their partner involved. Yeah. So how do, how do we change the biases? And you know, where I've talked to a guy who said, the word geek is reinforcing a bias or nerd where like, I use that term all the time, um, with science, is there, I mean, we had the, the lawsuit with Kleiner Perkins around the gender discrimination. She wasn't included. I mean, what's your take on all of this? I mean, how does someone practically take the data and put it into practice? >>I think the big thing is, you know, like I said, acknowledging that it exists, right? It's out there. We've been, I feel like our brains haven't necessarily adapted to the modern workplace and the challenges that we've dealt with because the modern workplace is something that was invented in the 1960s and our brains have evolved over a long time. So being able to handle some of the challenges that we have, especially on how men and women operate differently at the workplace, I think is key, but calling it out and making it okay to acknowledge it, but then counter where it needs to be countered where it's not right. And being explicit and having the conversations I think is the big piece. And that's what struck me with the Kleiner Perkins deal was let's have the conversation it's out there. A lot of times people are reticent to, to have the conversation because it's awkward and I need to be PC. And I'm worried about things. It's the elephant in the room, right. But it actually is. Dialogue is far better than leaving it. >>People are afraid. I mean, guys are afraid. Women are afraid. So it's a negative cycle. If it's not an out in the open, that's what I'm saying. >>And the idea is it's, what can we do collectively better to, to be more positive, to, to frame it more positively, because I think that makes a bigger difference in terms, in terms of talking about, Oh, we're different. How are we the same? How can we work together? What is the, the connection point that you bring, you bring, we all bring different skills and talents to the table. I think it's really taking a look at that and talking about it and calling it out and say, I'm not great at this. You're great at this. Let's, let's work together on what we can do, uh, more effectively, >>Okay. Team sports is great. And the diversity of workforce and tech is an issue. That's awesome. So I'd ask you to kind of a different question for both of you guys. What's the biggest surprise in the data and it could be what reinforced the belief or insight into something new share, uh, a surprise. Um, it could be pleasant or creepy or share it. >>Price to me is intuition. So we always talk about women having intuitions. I've had men say, you know, well, my wife is so intuitive. She kind of, she kinda gets that and I've had that in the workplace as well. And I think the biggest surprise for me was that we can now see, we've now proved the intuition. Intuition is a thing that women have, and it's about this kind of web thinking and connecting the dots. Yeah. So we sort of store these memories deep, deep inside. And then when we see something similar, we then make that connection. We call it intuition, but it's actually something it's a kind of a, you know, super recall if you like, and, and, and replaying that situation. But that I think was the biggest surprise to me, Amy. So I would think that the thing that, that always astonishes me is the workplace environment and how we set up environments sometimes to shoot ourselves in the foot. >>So, so often we'll set up, uh, a competitive environment, whatever it is, let's let's and it's internal competition. Well, it turns out that the way that the brain chemicals work in women is that competition actually froze us into, to stress or threat cycle much more easily than it does to men, but men need it to be able to get to optimal arousal. There's a lot of interesting research from Amy Ernest in, at Yale and, and that piece of how you can manipulate your environment to be more successful together to me is absolutely key. And being able to pull out elements of competition, but also elements of collaboration, you kind of knew it, but the science validates it and you go, this is why we need to make sure there's a balance between the two. So everyone's successful. So to me, that's the aha. I could listen to Amy all day and how we apply it to the workplace. That's the next big step. Yeah. >>Yeah. You guys are awesome. And thanks so much for sharing and I wish we could go long. We're getting the hook here on time, but is there any links and locations websites we can, people can go to to get more information on the studies, the science. So I, a lot of my day curating >>And looking for more research. So peak teams.com/blog is where I do a lot of my writing and suggestions. Um, it's peak teams, P E K T E M s.com. And so I run our blog and kind of put my musings every once in a while up there so that people can see what I'm working on. Um, but they can reach out at any time. And I'm on Twitter at, at peak teams geek. Speaking of geeks, I embraced the geek mentality, right? >>Well, we have, I think geeks comment personally, but, um, final point, I'll give you the last word, Amy, if you could have a magic wand to take the science and change the preferred vision of the future with respect to men and women, you know, working cohesively together, understanding that we're different decoupled in science. Now, what would you want to see for the environment work force, life balance? What would be the magic wand that you would change? >>I think being able to make women more confident by helping reduce bias with everybody. So being more keyed in to those biases that we have in those automatic things we do to shortcut and to be more aware of them and work on them together and not see them as bad, but see them as human. So I think that's my big takeaway is remove, remove more bias. >>Fantastic. Stella Lowe, and Amy Posey here inside the cube. Thanks so much. Congratulations on your great work. Great panel. We'll continue. Of course, we have a special channel on SiliconANGLE's dot TV for women in tech. Go to SiliconANGLE dot DV. We've got a lot of cube alumni. We had another one here today with Amy. Thank you for joining us. This is the cube. We'll be right back day three, bringing it to a close here inside the cube live in Las Vegas. I'm John Forney. We'll be right back after this short break.

>>The cube at IBM. Impact 2014 is brought to you by headline sponsor. IBM. Here are your hosts, John furrier and Paul Gillin. >>Hey, welcome back everyone. This is Silicon angle's the cube. It's our flagship program. We go out to the events district as soon from the noise. We're ending out day two of two days of wall to wall coverage with myself and Paul Galen. Uh, 10 to six 30 every day. I'm just, we'll take as much as we can just to get the data. Share that with you. Restrict the signal from the noise. I'm John furrier the bonus look at angle Miko is Paul Gilliam and our special guests, Holger Mueller, Mueller from constellation research analyst covering the space. Ray Wang was here earlier. You've been here for the duration. Um, we're going to break down the event. We'll do a wrap up here. Uh, we have huge impact event for 9,000 people. Uh, Paul, I want to go to you first and get your take on just the past two days. And we've got a lot of Kool-Aid injection attempts for Kool-Aid injection, but IBM people were very, very candid. I mean, I didn't find it, uh, very forceful at all from IBM. They're pragmatic. What's your thoughts on it? >>I think pragmatism is, is what I take away, John, if it gets a good, that's a good word for it. Uh, what I saw was a, uh, not a blockbuster. Uh, there was not a lot of, of, uh, of hype and overstatement about what the company was doing. I was impressed with Steve mills, but our interview with him yesterday, we asked about blockbuster acquisitions and he said basically, why, why, I mean, why should we take on a big acquisition that is going to create a headache, uh, for us in integrating into your organization? Let's focus on the spots where we have gaps and let's fill those. And that's really what they've, you know, they really have put their money where their mouth is and doing these 150 or more acquisitions over the last, uh, three or four years. Um, I think that the, the one question that I would have, I don't think there's any doubt about IBM's commitment to cloud as the future about their investment in big data analytics. They certainly have put their money where their mouth is. They're over $25 billion invested in big data analytics. One question I have coming out of this conference is about power and about the decision to exit the x86 market and really create confusion in a part of their business partners, their customers about about how they're going to fill that gap and where are they going to go for their actually needs and the power. Clearly power eight clearly is the future. It's the will fill that role in the IBM portfolio, but they've got to act fast. >>Do you think there's a ripple effect then so that that move I'll see cause a ripple effect in their ecosystem? >>Well, I was talking to a, I've talked to two IBM partners today, fairly large IBM partners and both of them have expressed that their customers are suffering some whiplash right now because all of a sudden the x86 option from IBM has gone away. And so it's frozen there. Their purchasing process and some of them are going to HP, some of them are looking at other providers. Um, I don't think IBM really has has told a coherent story to the markets yet about how >>and power's new. So they've got to prop that up. So you, so you're saying is okay, HP is going to get some new sales out of this, so frozen the for IBM and yet the power story's probably not clear. Is that what you're hearing? >>I don't think the power story is clear. I mean certainly it was news to me that IBM is taking on Intel at the, at this event and I was surprised that, that, >>that that was a surprise. Hold on, I've got to go to you because we've been sitting here the Cuban, we've been having all the execs come here and we've been getting briefed here in the cube. Shared that with the audience. You've been out on the ground, we've bumped into you guys, all, all the other analysts and all the briefings you've been in, the private sessions you've been in the rooms you've been, you've been, you've been out, out in the trenches there. What have you, what are you finding, what have you been hearing and what are the, some of the soundbites that you could share with the audience? It's not the classic God, Yemen, what are the differences? >>The Austin executives in cloud pedal, can you give me your body language? He had impact one year ago because they didn't have self layer at a time, didn't want to immediately actionable to do something involving what? A difference things. What in itself is fine, but I agree with what you said before is the messaging is they don't tell the customers, here's where we are right now. Take you by the hand. It's going to be from your door. And there's something called VMs. >>So it's very interesting. I mean I would consider IBM finalized the acquisition only last July. It's only been nine months since was acquired. Everything is software now. It leads me to think of who acquired who IBM acquired a software or did soflar actually acquire IBM because it seems to, SoftLayer is so strategic. IBM's cloud strategy going forward. >>Very strategic. I think it's probably why most transformative seemed like the Nexans agenda. And you've heard me say assault on a single thing. who makes it seven or eight weeks ago? It's moving very far. >>What do you think about the social business? Is that hanging together, that story? Hang on. It's obviously relevant direction. It's kind of a smarter planet positioning. Certainly businesses will be social. Are you seeing any meat on the bone there? On the collaboration side, >>one of the weakest parts, they have to be built again. Those again, they also have an additional for HR, which was this position, this stuff. It's definitely something which gives different change. >>I have to say, John, I was struck by the lack of discussion of social business in the opening keynote in particular a mobile mobile, big data. I mean that that came across very clear, but I've been accustomed to hearing that the social business rugby, they didn't, it didn't come out of this conference. >>Yeah. I mean my take on that was, is that >>I think it's pretty late. I don't think there's a lot of meat in the bone with the social, and I'll tell you why. I think it's like it's like the destination everyone wants to go to, but there's no really engine yet. Right. I think there's a lot of bicycle riding when they need a car. Right? So the infrastructure is just not is too embryonic, if you will. A lot of manual stuff going on. Even the analytics and you know you're seeing in the leaderboard here in the social media side and big data analytics. Certainly there are some core engine parts around IBM, but that social engine, I just don't see it happening. You risk requires a new kind of automation. It's got some real times, but I think that this is some, some nice bright spots. I love the streams. I love this zone's concept that we heard from Watson foundations. >>I think that is something that they need to pull out the war chest there and bring that front and center. I think the thinking about data as zones is really compelling and then I'll see mobile, they've got all the messaging on that and to give IBM to the benefit of the doubt. I mean they have a story now that they have a revenue generating story with cloud and with big data and social was never a revenue generating story. That's a software story. It's not big. It's not big dollars. And they've got something now that really they're really can drive. >>I'll tell you Chris Kristin from mobile first. She was very impressive and, and I'll tell you that social is being worked on. So I put the people are getting it. I mean IBM 100% gets social. I think the, the, it's not a gimmick to them. It's not like, Oh, we got some social media stuff. I think in the DNA of their soul, they, they come from that background of social. So I give them high marks on that. I just don't see the engine yet. I'm looking for analytics. I'm looking for a couple of eight cylinders. I just don't see it yet. You know, the engine, the engines, lupus and she wants to build the next generation of education. Big data, tons of mobile as the shoulder equivalent to social. I'm skeptical. I'm skeptical on Bloomix. I'll tell you why. I'm not skeptical. I shouldn't say that. >>It's going to get some plane mail for that. Okay. I'll say I'll see what's out there. I'll say it. I'm skeptical of Blumix because it could be a Wright brothers situation. Okay, look, I'm wrong guys building the wrong airplane. So the question is they might be on the wrong side of history if they don't watch the open source foundations because here's the problem. I have a blue mix, gets rushed to the market. Certainly IBM has got muscle solutions together. No doubt debting on cloud Foundry is really a risk and although people are pumping it up and it's got some momentum, they don't have a big community, they have a lot of marketing behind it and I know Jane's Wars over there is doing a great job and I'm Josh McKinsey over there with piston cloud. It'll behind it. It has all the elements of open collaboration and architecture or collaboration. However, if it's not a done deal yet in my mind, so that's a, that is a risk factor in my my mind. >>We've met a number of amazing, maybe you can help to do, to put these in order, a number of new concepts out there. We've got Bloomex the soft player, and we've got the marketplace, and these are all three concepts that approval, which is a subset of which, what's the hierarchy of these different platforms? >>That's hopefully, that's definitely at the bottom. The gives >>us visibility. You talk about the CIO and CSI all the time. Something you securities on every stupid LCO one on OCS and the marketplace. Basically naming the applications. Who would folded? IBM. IBM would have to meet opensource platform as a service. >>Well, it's not, even though it's not even open source and doing a deal with about foundries, so, so they've got, I think they're going in the middle. Where's their angle on that? But again, I like, again, the developer story's good, the people are solid. So I think it's not a fail of my, in my mind that all the messaging is great. But you know, we went to red hat summit, you know, they have a very active community, multiple generations in the data center, in the Indiana prize with Linux and, and open, you know, they're open, open shift is interesting. It's got traction and it's got legit traction. So that's one area. The other area I liked with Steve mills was he's very candid about this turf. They're staking out. Clearly the cloud game is up, is there is hardcore for them and in the IBM flavor enterprise cloud, they want to win the enterprise cloud. They clearly see Amazon, they see Amazon and its rhetoric and Grant's narrative and rhetoric against Amazon was interesting saying that there's more links on SoftLayer and Amazon. Now if you count links, then I think that number is skewed. So it's, you know, there's still a little bit of gamification going to have to dig into that. I didn't want to call him out on that, but know there's also a hosting business versus, you know, cloud parse the numbers. But what's your take on Amazon soft layer kind of comparison. >>It's, it's fundamentally different, right? Mustn't all shows everything. Why did see retailers moves is what to entirely use this software, gives them that visibility machine, this accommodation more conservatively knowing that I buy them, I can see that I can even go and physically touch that machine and I can only did the slowly into any cloud virtualization shed everything. >>Oh, Paul, I gotta say my favorite interview and I want to get your take on this. It was a Grady food. She was sat down with us and talk with us earlier today. IBM fell up, walks on water with an IBM Aussie legend in the computer industry. Just riveting conversation. I mean, it was really just getting started. I mean, it felt like we were like, you know, going into cruising altitude and then he just walked away. So they w what's your take on that conversation? >>Well, I mean, certainly he, uh, the gritty boujee interview, he gave us the best story of, of the two days, which is, uh, they're being in the hospital for open heart surgery, looking up, seeing the equipment, and it's going to be used to go into his chest and open his heart and knowing that he knows the people who program that, that equipment and they programmed it using a methodology that he invented. Uh, that, that, that's a remarkable story. But I think, uh, uh, the fact that that a great igloo can have a job at a company like IBM is a tribute to IBM. The fact that they can employ people like that who don't have a hard revenue responsibility. He's not a P. and. L, he's just, he's just a genius and he's a legend and he's an IBM to its crude, finds a place for people like that all throughout his organization. >>And that's why they never lost their soul in my opinion. You look at what HP and IBM, you know, IBM had a lot of reorganizations, a lot of pivots, so to speak, a lot of battleship that's turned this in way. But you know, for the most part they kept their R and D culture. >>But there's an interesting analogy too. Do you remember the case methodology was mutual support of them within the finance language that you mailed something because it was all about images, right? You would use this, this methodology, different vendors that were prior to the transport itself. Then I've yet to that credit, bring it together. bring and did a great service to all for software engineering. And maybe it's the same thing at the end, can play around diversity. >>You've got to give IBM process a great point. Earlier we, Steve mills made a similar reference around, it wasn't animosity, it was more of Hey, we've helped make Intel a big business, but the PC revolution, you know, where, what's in it for us? Right? You know, where's our, you know, help us out, throw us a bone. Or you know, you say you yell to Microsoft to go of course with the licensing fee with Gates, but this is the point, the unification story and with grays here, you know IBM has some real good cultural, you know industry Goodwill, you agree >>true North for IBM is the Antal quest customer. They'll do what's right where the money and the budget of the enterprise customers and press most want compatibility. They don't want to have staff, of course they want to have investment protection >>guys. I'd be able to do a good job of defining that as their cloud strategy that clearly are not going head to head with Amazon. It's a hybrid cloud strategy. They want to, they see the enterprise customers that legacy as as an asset and it's something they want to build on. Of course the risk of that is that Amazon right now is the pure play. It has all the momentum. It has all the buzz and and being tied to a legacy is not always the greatest thing in this industry, but from a practical revenue generating standpoint, it's pretty good. >>Hey guys, let's go down and wrap up here and get your final thoughts on the event. Um, and let's just go by the numbers, kind of the key things that IBM was promoting and then our kind of scorecard on kind of where they, where they kind of played out and new things that popped out of the woodwork that got your attention. You see the PO, the power systems thing was big on their messaging. Um, the big data story continues to be part of it. Blue mix central to the operations and the openness. You had a lot of open, open openness in their messaging and for the most part that's pretty much it. Um, well Watson, yeah, continue. Agents got up to Watson. >>Wow. A lot of news still to come out of Watson I think in many ways that is their, is their ACE in the hole and then that is their diamond. Any other thoughts? >>Well, what I missed is, which I think sets IBM apart from this vision, which is the idea of the API. Everybody else at that pure name stops the platform or says, I'm going to build like the org, I'm going to build you. That's a clear differentiator on the IBM side, which you still have to build part. They still have to figure out granularity surface that sets them apart that they have to give one. >>Yeah, and I think I give him an a plus on messaging. I think they're on all the right fault lines on the tectonic shifts that we're seeing. Everyone, I asked every every guest interview, what's the game changing moment? Why is it so important? And almost consistently the answers were, you know, we're living in a time of fast change data, you know, efficiency spare or you're going to be left behind. This is the confluence of all these trends, these fall lines. So I think IBM is sitting on these fall lines. Now the question is how fast can they cobbled together the tooling from the machineries that they have built over the years. Going back to the mainframe anniversary, it's out there. A lot of acquisitions, but, but so far the story and the story >>take the customer by the hand. That's the main challenge. I see. This wasn't often we do in Mexico, they want zero due to two times or they're chilling their conferences. It's the customer event and you know, and it's 9,000 people somehow have to do something to just show, right? So why is my wave from like distinguished so forth and so and so into? Well Lou mentioned, sure for the cloud, but how do we get there, right? What can we use, what am I SS and leverage? How do I call >>guys, really appreciate the commentary. Uh, this is going to be a wrap for us when just do a shout out to Matt, Greg and Patrick here doing a great job with the production here in the cube team and we have another cube team actually doing a simultaneous cube up in San Francisco service. Now you guys have done a great job here. And also shout out to Bert Latta Moore who's been doing a great job of live tweeting and help moderate the proud show, which was really a huge success and a great crowd chat this time. Hopefully we'll get some more influencers thought leaders in there for the next event and of course want to thank Paul Gillen for being an amazing cohost on this trip. Uh, I thought the questions and the and the cadence was fantastic. The guests were happy and hold there. Thank you for coming in on our wrap up. >>Really appreciate it. Constellation research. Uh, this is the cube. We are wrapping it up here at the IBM impact event here live in Las Vegas. It's the cube John furrier with Paul Gillen saying goodbye and see it. Our next event and stay tuned if it's look at angel dot DV cause we have continuous coverage of service now and tomorrow we will be broadcasting and commentating on the Facebook developer conference in San Francisco. We're running here, Mark Zuckerberg and all Facebook's developers and all their developer programs rolling out. So watch SiliconANGLE TV for that as well. Again, the cube is growing with thanks to you watching and thanks to all of our friends in the industry. Thanks for watching..