(low-key music) >> The cube's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Welcome back to Spain, everybody. My name's Dave Vellante. I'm here with Dave Nicholson. We are live at the Fira in Barcelona, covering MWC23 day four. We've been talking about, you know, 5G all week. We're going to talk about it some more. Jillian Kaplan is here. She's the head of Global Telecom Thought Leadership at Dell Technologies, and we're pleased to have Meg Knauth, who's the Vice President for Digital Platform Engineering at T-Mobile. Ladies, welcome to theCUBE. Thanks for coming on. >> Thanks for having us. >> Yeah, thank you. >> All right, Meg, can you explain 5G and edge to folks that may not be familiar with it? Give us the 101 on 5G and edge. >> Sure, I'd be happy to. So, at T-Mobile, we want businesses to be able to focus on their business outcomes and not have to stress about network technology. So we're here to handle the networking behind the scenes for you to achieve your business goals. The main way to think about 5G is speed, reduced latency, and heightened security. And you can apply that to so many different business goals and objectives. You know, some of the use cases that get touted out the most are in the retail manufacturing sectors with sensors and with control of inventory and things of that nature. But it can be applied to pretty much any industry because who doesn't need more (chuckles) more speed and lower latency. >> Yeah. And reliability, right? >> Exactly. >> I mean, that's what you're going to have there. So it's not like it's necessarily going to- you know, you think about 5G and these private networks, right? I mean, it's not going to, oh, maybe it is going to eat into, there's a Venn there, I know, but it's not going to going to replace wireless, right? I mean, it's new use cases. >> Yeah. >> Maybe you could talk about that a little bit. >> Yeah, they definitely coexist, right? And Meg touched a little bit on like all the use cases that are coming to be, but as we look at 5G, it's really the- we call it like the Enterprise G, right? It's where the enterprise is going to be able to see changes in their business and the way that they do things. And for them, it's going to be about reducing costs and heightening ROI, and safety too, right? Like being able to automate manufacturing facilities where you don't have workers, like, you know, getting hit by various pieces of equipment and you can take them out of harm's way and put robots in their place. And having them really work in an autonomous situation is going to be super, super key. And 5G is just the, it's the backbone of all future technologies if you look at it. We have to have a network like that in order to build things like AI and ML, and we talk about VR and the Metaverse. You have to have a super reliable network that can handle the amount of devices that we're putting out today, right? So, extremely important. >> From T-Mobile's perspective, I mean we hear a lot about, oh, we spent a lot on CapEx, we know that. You know, trillion and a half over the next seven years, going into 5G infrastructure. We heard in the early keynotes at MWC, we heard the call to you know, tax the over the top vendors. We heard the OTT, Netflix shot back, they said, "Why don't you help us pay for the content that we're creating?" But, okay, so I get that, but telcos have a great business. Where's T-Mobile stand on future revenue opportunities? Are you looking to get more data and monetize that data? Are you looking to do things like partner with Dell to do, you know, 5G networks? Where are the opportunities for T-Mobile? >> I think it's more, as Jillian said, it's the opportunities for each business and it's unique to those businesses. So we're not in it just for ourselves. We're in it to help others achieve their business goals and to do more with all of the new capabilities that this network provides. >> Yeah, man, I like that answer because again, listening to some of the CEOs of the large telcos, it's like, hmm, what's in it for me as the customer or the business? I didn't hear enough of that. And at least in the early keynotes, I'm hearing it more, you know, as the show goes on. But I don't know, Dave, what do you think about what you've heard at the event? >> Well, I'm curious from T-Mobile's perspective, you know when a consumer thinks about 5G, we think of voice, text, and data. And if we think about the 5G network that you already have in place, I'm curious, if you can share this kind of information, what percentage of that's being utilized now? How much is available for the, you know, for the Enterprise G that we're talking about, and maybe, you know, in five years in the future, do you have like a projected mix of consumer use versus all of these back office, call them processes that a consumer's not aware of, but you know the factory floor being connected via 5G, that frontiers that emerges, where are we now and what are you looking towards? Does that make sense? Kind of the mixed question? >> Hand over the business plan! (all laugh) >> Yeah! Yeah, yeah, yeah. >> Yeah, I- >> I want numbers Meg, numbers! >> Wow. (Dave and Dave laugh) I'm probably actually not the right person to speak to that. But as you know, T-Mobile has the largest 5G network in North America, and we just say, bring it, right? Let's talk- >> So you got room, you got room for Jillian's stuff? >> Yeah, let's solve >> Well, we can build so many >> business problems together. >> private 5G networks, right? Like I would say like the opportunities are... There's not a limit, right? Because as we build out these private networks, right? We're not on a public network when we're talking about like connecting these massive factories or connecting like a retail store to you and your house to be able to basically continue to try on the clothes remotely, something like that. It's limitless and what we can build- >> So they're related, but they're not necessarily mutually exclusive in the sense that what you are doing in the factory example is going to interfere with my ability to get my data through T-mobile. >> No, no, I- >> These are separated. >> Yeah. Yeah. >> Okay. >> As we build out these private networks and these private facilities, and there are so many applications in the consumer space that haven't even been realized yet. Like, when we think about 4G, when 4G launched, there were no applications that needed 4G to run on our cell phones, right? But then the engineers got to work, right? And we ended up with Uber and Instagram stories and all these applications that require 4G to launch. And that's what's going to happen with 5G too, it's like, as the network continues to get built, in the consumer space as well as the enterprise space, there's going to be new applications realized on this is all the stuff that we can do with this amazing network and look how many more devices and look how much faster it is, and the lower latency and the higher bandwidth, and you know, what we can really build. And I think what we're seeing at this show compared to last year is this stuff actually in practice. There was a lot of talk last year, like about, oh, this is what we can build, but now we're building it. And I think that's really key to show that companies like T-Mobile can help the enterprise in this space with cooperation, right? Like, we're not just talking about it now, we're actually putting it into practice. >> So how does it work? If I put in a private network, what are you doing? You slice out a piece of the network and charge me for it and then I get that as part of my private network. How does it actually work for the customer? >> You want to take that one? >> So I was going to say, yeah, you can do a network slice. You can actually physically build a private network, right? It depends, there's so many different ways to engineer it. So I think you can do it either way, basically. >> We just, we don't want it to be scary, right? >> Yep. >> So it starts with having a conversation about the business challenges that you're facing and then backing it into the technology and letting the technology power those solutions. But we don't want it to be scary for people because there's so much buzz around 5G, around edge, and it can be overwhelming and you can feel like you need a PhD in engineering to have a conversation. And we just want to kind of simplify things and talk in your language, not in our language. We'll figure out the tech behind the scenes. Just tell us what problems we can solve together. >> And so many non-technical companies are having to transform, right? Like retail, like manufacturing, that haven't had to be tech companies before. But together with T-Mobile and Dell, we can help enable that and make it not scary like Meg said. >> Right, so you come into my factory, I say, okay, look around. I got all these people there, and they're making hoses and they're physically putting 'em together. And we go and we have to take a physical measurement as to, you know, is it right? And because if we don't do that, then we have to rework it. Okay, now that's a problem. Okay, can you help me digitize that business? I need a network to do that. I'm going to put in some robots to do that. This is, I mean, I'm making this up but this has got to be a common use case, right? >> Yeah. >> So how do you simplify that for the business owner? >> So we start with what we can provide, and then in some cases you need additional solution providers. You might need a robotics company, you might need a sensor company. But we have those contacts to bring that together for you so that you don't have to be the expert in all those things. >> And what do I do with all the data that I'm collecting? Because, you know, I'm not really a data expert. Maybe, you know, I'm good at putting hoses together, but what's the data layer look like here? (all laughing) >> It's a hose business! >> I know! >> Great business. >> Back to the hoses again. >> There's a lot of different things you can do with it, right? You can collect it in a database, you can send it up to a cloud, you can, you know, use an edge device. It depends how we build the network. >> Dave V.: Can you guys help me do that? Can you guys- >> Sure, yeah. >> Help me figure that out. Should I put it into cloud? Should I use this database or that data? What kind of skills do I need? >> And it depends on the size of the network, right? And the size of the business. Like, you know, there's very simple. You don't have to be a massive manufacturer in order to install this stuff. >> No, I'm asking small business questions. >> Yeah. >> Right, I might not have this giant IT team. I might not have somebody who knows how to do ETL and PBA. >> Exactly. And we can talk to you too about what data matters, right? And we can, together, talk about what data might be the most valuable to you. We can talk to you about how we use data. But again, simplifying it down and making it personal to your business. >> Your point about scary is interesting, because no one has mentioned that until you did in four days. Three? Four days. Somebody says, let's do a private 5G network. That sounds like you're offering, you know, it's like, "Hey, you know what we should do Dave? We'll build you a cruise ship." It's like, I don't need a cruise ship, I just want to go bass fishing. >> Right, right, right. >> But in fact, these things are scalable in the sense that it can be scaled down from the trillions of dollars of infrastructure investment. >> Yeah. >> Yeah. It needs to be focused on your outcome, right? And not on the tech. >> When I was at the Dell booth I saw this little private network, it was about this big. I'm like, how much is that? I want one of those. (all laugh) >> I'm not the right person to talk about that! >> The little black one? >> Yes. >> I wanted one of those, too! >> I saw it, it had a little case to carry it around. I'm like, that could fit in my business. >> Just take it with you. >> theCUBE could use that! (all laugh) >> Anything that could go in a pelican case, I want. >> It's true. Like, it's so incredibly important, like you said, to focus on outcomes, right? Not just tech for the sake of tech. What's the problem? Let's solve the problem together. And then you're getting the outcome you want. You'll know what data you need. If you know what the problem is, you're like, okay this is the data I need to know if this problem is solved or not. >> So it sounds like 2022 was the year of talking about it. 2023, I'm inferring is the year of seeing it. >> Yep. >> And 2024 is going to be the year of doing it? >> I think we're doing it now. >> We're doing it now. >> Yeah. >> Okay. >> Yeah, yeah. We're definitely doing it now. >> All right. >> I see a lot of this stuff being put into place and a lot more innovation and a lot more working together. And Meg mentioned working with other partners. No one's going to do this alone. You've got to like, you know, Dell especially, we're focused on open and making sure that, you know, we have the right software partners. We're bringing in smaller players, right? Like ISVs too, as well as like the big software guys. Incredibly, incredibly important. The sensor companies, whatever we need you've got to be able to solve your customer's issue, which in this case, we're looking to help the enterprise together to transform their space. And Dell knows a little bit about the enterprise, so. >> So if we are there in 2023, then I assume 2024 will be the year that each of your companies sets up a dedicated vertical to address the hose manufacturing market. (Meg laughing) >> Oh, the hose manufacturing market. >> Further segmentation is usually a hallmark of the maturity of an industry. >> I got a lead for you. >> Yeah, there you go. >> And that's one thing we've done at Dell, too. We've built like this use case directory to help the service providers understand what, not just say like, oh, you can help manufacturers. Yeah, but how, what are the use cases to do that? And we worked with a research firm to figure out, like, you know these are the most mature, these are the best ROIs. Like to really help hone in on exactly what we can deploy for 5G and edge solutions that make the most sense, not only for service providers, right, but also for the enterprises. >> Where do you guys want to see this partnership go? Give us the vision. >> To infinity and beyond. To 5G! (Meg laughing) To 5G and beyond. >> I love it. >> It's continuation. I love that we're partnering together. It's incredibly important to the future of the business. >> Good deal. >> To bring the strengths of both together. And like Jillian said, other partners in the ecosystem, it has to be approached from a partnership perspective, but focused on outcomes. >> Jillian: Yep. >> To 5G and beyond. I love it. >> To 5G and beyond. >> Folks, thanks for coming on theCUBE. >> Thanks for having us. >> Appreciate your insights. >> Thank you. >> All right. Dave Vellante for Dave Nicholson, keep it right there. You're watching theCUBE. Go to silliconANGLE.com. John Furrier is banging out all the news. theCUBE.net has all the videos. We're live at the Fira in Barcelona, MWC23. We'll be right back. (uplifting electronic music)

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>Okay. Today we welcome Evan Kaplan, CEO of Influx Data, the company behind Influx DB Welcome, Evan. Thanks for coming on. >>Hey, John. Thanks for having me. >>Great segment here on the influx. DB Story. What is the story? Take us through the history. Why Time series? What's the story? >>So the history of history is actually actually pretty interesting. Paul Dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on Wall Street building a number of times series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you have to do a tonne of work just to start doing work. Which means you have to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimised for a trading platform or a time series platform. And he sort of he just developed This real clear point of view is this is not how developers should work. And so in 2013, he went through y Combinator and he built something for he made his first commit to open source influx TB at the end of 2013. And basically, you know, from my point of view, you invented modern time series, which is you start with a purpose built time series platform to do these kind of work clothes, and you get all the benefits of having something right out of the box or developer can be totally productive right away. >>And how many people in the company What's the history of employees and stuff? Yeah, >>I think we're you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company I joined the company in 2016 and I love Paul's vision, and I just had a strong conviction about the relationship between Time series and Iot. Because if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light. They're measuring their instrumented something over time. And so I thought that would be super relevant over long term, and I've not regretted. Oh, >>no, and it's interesting at that time to go back in history. You know the role of databases are relational database, the one database to rule the world. And then, as clouds started coming in, you're starting to see more databases, proliferate types of databases. And Time series in particular, is interesting because real time has become super valuable. From an application standpoint, Iot, which speaks Time series, means something. It's like time matters >>times, >>and sometimes date is not worth it after the time. Sometimes it's worth it. And then you get the Data lake, so you have this whole new evolution. Is this the momentum? What's the momentum? I guess the question is, what's the momentum behind >>what's causing us to grow? So >>the time series. Why is time series in the category momentum? What's the bottom line? We'll >>think about it. You think about it from abroad, abroad, sort of frame, which is where what everybody's trying to do is build increasingly intelligent systems, whether it's a self driving car or a robotic system that does what you want to do or self healing software system. Everybody wants to build increasing intelligence systems, and so, in order to build these increasingly intelligence systems. You have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened? What happened? What happened? What's going to happen? And so you get to these applications, like predictive maintenance or smarter systems. And increasingly, you want to do that stuff not just intelligently, but fast in real time, so millisecond response, so that when you're driving a self driving car and the system realises that you're about to do something, essentially, you want to be able to act in something that looks like real time. All systems want to do that. I want to be more intelligent, and they want to be more real time. So we just happened to, you know, we happen to show up at the right time. In the evolution of the market. >>It's interesting. Near real time isn't good enough when you need real time. Yeah, >>it's not, it's not, and it's like it's like everybody wants even when you don't need it. Uh, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature even though you're not going to use it, you decide that you're buying criteria. Real time is a buying criteria. >>So what you're saying, then is near real time is getting closer to real time as possible as possible. Okay, so talk about the aspect of data cause we're hearing a lot of conversations on the Cubans particular around how people are implementing and actually getting better. So iterating on data. >>But >>you have to know when it happened to get know how to fix it. So this is a big part of what we're seeing with people saying, Hey, you know, I want to make my machine learning albums better after the fact I want to learn from the data. Um, how does that How do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure, So for sure, what you're saying is is none of this is non linear. It's all incremental. And so if you take something, you know, just as an easy example. If you take a self driving car, what you're doing is your instrument in that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens. Oh, that's wrong. Oh, I have to correct for that. Correct for that in the software, if you do that four billion times, you get a self driving car. But every system moves along that evolution. And so you get the dynamic of you know of constantly instrumented, watching the system behave and do it and this and sets up driving cars. One thing. But even in the human genome, if you look at some of our customers, you know people like, you know, people doing solar arrays. People doing power walls like all of these systems, are getting smarter. >>What are the top application? What are you seeing your with Influx DB The Time series. What's the sweet spot for the application use case and some customers give some examples. >>Yeah, so it's pretty easy to understand. On one side of the equation. That's the physical side is sensors are the sensors are getting cheap. Obviously, we know that, and they're getting. The whole physical world is getting instrumented your home, your car, the factory floor, your wrist watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time, and so there are three or four sweet spots for us. But they're all on that side. They're all about Iot. So they're talking about consumer Iot projects like Google's Nest Tato Um, particle sensors, Um, even delivery engines like Happy who deliver the interesting part of South America. Like anywhere. There's a physical location doing that's on the consumer side. And then another exciting space is the industrial side. Factories are changing dramatically over time, increasingly moving away from proprietary equipment to develop or driven systems that run operational because what it has to get smarter when you're building, when you're building a factory, systems all have to get smarter. And then lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid motors, Nicola Motors, um you know, lots to do with electric cars, solar arrays, windmills are raised just anything that's going to get instrumented, that where that instrumentation becomes part of what the purpose is. >>It's interesting. The convergence of physical and digital is happening with the data Iot you mentioned. You know, you think of Iot. Look at the use cases there. It was proprietary OT systems now becoming more I p enabled Internet protocol and now edge compute getting smaller, faster, cheaper ai going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing Iot going to a new level? What was that? What's the Iot? Where's the Iot dots connecting to? Because, you know, as these two cultures merge operations basically industrial factory car, they gotta get smarter. Intelligent edge is a buzzword, but it has to be more intelligent. Where's the where's the action in all this? So the >>action really, really at the core? >>It's >>at the developer, right, Because you're looking at these things. It's very hard to get off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developers. And so what you're seeing is a movement in the world that that maybe you and I grew up in with I t r o T moving increasingly that developer driven capability. And so all of these Iot systems, their bespoke, they don't come out of the box. And so the developer and the architect, the CTO they define what's my business? What am I trying to do trying to sequence the human genome and figure out when these genes express themselves? Or am I trying to figure out when the next heart rate monitor is going to show up in my apple watch, right? What am I trying to do? What's the system I need to build? And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right, used to be used by an application or a service or a sad thing for But with this dynamic with this integration of systems, it's all about bespoke. It's all about building something. >>So let's get to the death of a real quick, real highlight point. Here is the data. I mean, I could see a developer saying, Okay, I need to have an application for the edge Iot, edge or car. I mean, we're gonna test look at applications of the cars right there. I mean, there's the modern application lifecycle now, so take us through how this impacts the developer doesn't impact their CI CD. Pipeline is a cloud native. I mean, where does this all Where does this go to? >>Well, so first of all you talking about, there was an internal journey that we had to go through as a company, which which I think is fascinating for anybody's interested as we went from primarily a monolithic software that was open source to building a cloud native platform, which means we have to move from an agile development environment to a C I C d. Environ. So two degree that you're moving your service whether it's, you know, Tesla, monitoring your car and updating your power walls right? Or whether it's a solar company updating your race right to the degree that services cloud then increasingly removed from an agile development to a CI CD environment which is shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also going to happen in a big way >>when your customer base that you have now and you see evolving with influx DB is it that they're gonna be writing more of the application or relying more on others? I mean, obviously the open source component here. So when you bring in kind of old way new Way Old Way was, I got a proprietary platform running all this Iot stuff and I got to write, Here's an application. That's general purpose. I have some flexibility, somewhat brittle. Maybe not a lot of robustness to it, but it does its job >>a good way to think about this. >>This is what >>So, yeah, a good way to think about this is what What's the role of the developer slashed architect C T o that chain within a large enterprise or a company. And so, um, the way to think about is I started my career in the aerospace industry, and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts instead. What they do is they assemble, they buy the wings, they buy the engines they assemble. Actually, they don't buy the wings. It's the one thing they buy, the material of the way they build the wings because there's a lot of tech in the wings and they end up being assemblers, smart assemblers of what ends up being a flying aeroplane, which is pretty big deal even now. And so what happens with software people is they have the ability to pull from, you know, the best of the open source world, so they would pull a time series capability from us. Then they would assemble that with potentially some E t l logic from somebody else, or they assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers. But they become masters of that bespoke application, and I think that's where it goes because you're not writing native code for everything, >>so they're more flexible. They have faster time to market because they're assembling way faster and they get to still maintain their core competency. OK, the wings. In this case, >>they become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who started build stuff. By the way. This is not different than the people have just up the road Google have been doing for years or the tier one Amazon building all their own. >>Well, I think one of the things that's interesting is that this idea of a systems developing a system architecture, I mean systems, uh, systems have consequences when you make changes. So when you have now cloud data centre on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing. That's exactly >>that's where that's where the, you know that that Boeing or that aeroplane building analogy comes in for us. We've really been thoughtful about that because I o. T. It's critical. So are open Source Edge has the same API as our cloud native stuff that hasn't enterprise on premises or multiple products have the same API, and they have a relationship with each other. They can talk with each other, so the builder builds at once. And so this is where when you start thinking about the components that people have to use to build these services is that you want to make sure at least that base layer that database layer that those components talk to each other. >>We'll have to ask you. I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>I mean, you have appeal for >>a big check blank check. If you can answer this question only if you get the question right. I got all this important operation stuff. I got my factory. I got my self driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about Time Series? Because now I have to make these architectural decisions as you mentioned and it's going to impact my application development. So huge decision point for your customers. What should I care about the most? What's in it for me? Why is time series important? Yeah, >>that's a great question. So chances are if you've got a business that was 20 years old or 25 years old, you're already thinking about Time series. You probably didn't call it that you built something on a work call or you build something that IBM db two. Right, and you made it work within your system, right? And so that's what you started building. So it's already out there. There are, you know, they're probably hundreds of millions of Time series applications out there today. But as you start to think about this increasing need for real time and you start to think about increasing intelligence, you think about optimising those systems over time. I hate the word but digital transformation, and you start with Time series. It's a foundational base layer for any system that you're going to build. There's no system I can think of where time series shouldn't be the foundational base layer. If you just want to store your data and just leave it there and then maybe look it up every five years, that's fine. That's not time. Serious time series when you're building a smarter, more intelligent, more real time system, and the developers now know that, and so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a P o for you in a big check, what what's the value to me as like when I implement this What's the end state? What's it look like when it's up and running? What's the value proposition for me? What's in it? >>So when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data transforming it in near real time. So the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling intelligence system. I think that's what developers and architects are seeing now. >>Bottom line. Final word. What's in it for the customer? What's what's your What's your statement of the customer? Would you say to someone looking to do something in time, series and edge? >>Yeah. So it's pretty clear to clear to us that if you're building, if you view yourself as being in the building business of building systems that you want them to be increasingly intelligent, self healing, autonomous, you want them to operate in real time that you start from Time series. I also want to say What's in it for us in flux? What's in it for us is people are doing some amazing stuff. I highlighted some of the energy stuff, some of the human genome, some of the health care. It's hard not to be proud or feel like. Wow. Somehow I've been lucky. I've arrived at the right time in the right place, with the right people to be able to deliver on that. That's That's also exciting on our side of the equation. >>It's critical infrastructure, critical critical operations. >>Yeah, great >>stuff. Evan. Thanks for coming on. Appreciate this segment. All right. In a moment. Brian Gilmore, director of Iot and emerging Technology that influx, they will join me. You're watching the Cube leader in tech coverage. Thanks for watching

(upbeat music) >> Okay today, we welcome Evan Kaplan, CEO of InfluxData, the company behind InfluxDB. Welcome Evan, thanks for coming on. >> Hey John, thanks for having me. >> Great segment here on the InfluxDB story. What is the story? Take us through the history, why time series? What's the story? >> So the history history is actually pretty interesting. Paul Dix my partner in this and our founder, super passionate about developers and developer experience. And he had worked on wall street building a number of time series kind of platform, trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave. Which means you had to do a ton of work just to start doing work. Which means you had to write a bunch of extrinsic routines, you had to write a bunch of application handling on existing relational databases, in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view. This is not how developers should work. And so in 2013, he went through Y Combinator, and he built something for, he made his first commit to open source InfluxDB in the end of 2013. 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 out of the box. So a developer can be totally productive right away. >> And how many people are in the company? What's the history of employees is there? >> Yeah, I think we're, you know, I always forget the number but something like 230 or 240 people now. I joined the company in 2016, and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. 'Cause if you think about it, what sensors do is they speak time series. Pressure, temperature, volume, humidity, light, they're measuring, they're instrumenting something over time. And so I thought that would be super relevant over the long term, and I've not regretted it. >> Oh no, and it's interesting at that time if you go back in history, you know, the role of database. It's all relational database, the one database to rule the world. And then as cloud started coming in, you started to see more databases proliferate, types of databases. And time series in particular is interesting 'cause real time has become super valuable from an application standpoint. IOT which speaks time series, means something. It's like time matters >> Times yeah. >> And sometimes data's not worth it after the time, sometimes it's worth it. And then you get the data lake, so you have this whole new evolution. Is this the momentum? What's the momentum? I guess the question is what's the momentum behind it? >> You mean what's causing us to grow so fast? >> 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 sort of frame which is, what everybody's trying to do is build increasingly intelligent systems. whether it's a self-driving car or a robotic system that does what you want to do, or a self-healing software system. Everybody wants to build increasing intelligent systems. And so in order to build these 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 going to happen. And so you get to these applications like predictive maintenance, or smarter systems, and increasingly you want to do that stuff not just intelligently, but fast in real time. So millisecond response, so that when you're driving a self-driving car, and the system realizes that you're about to do something, essentially you want to be able to act in something that looks like real time. All systems want to do that, they 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. >> Yeah, it's not, it's not. And it's like everybody wants real 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 going to use it. You decide that's your buying criteria. Real time is criteria for people. >> So I mean, what you're saying then is near realtime is getting closer to real time as fast as possible? >> Right. >> Okay, so talk about the aspect of data, 'cause we're hearing a lot of conversations on theCUBE in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get know how to fix it. So this is a big part of what we're seeing with people saying, "Hey, you know I want to "make my machine learning algorithms better "after the fact, I want to learn from the data." 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 what you're saying is, is none of this is non-linear, it's all incremental. And so if you take something, you know just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop which is, I instrument it, I watch what happens, oh that's wrong, oh I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car. But every system moves along that evolution. And so you get the dynamic of constantly instrumenting, watching the system behave and do it. And so a self driving car is one thing, but even in the human genome, if you look at some of our customers, you know, people like, people doing solar arrays, people doing power walls like all of these systems are getting smarter and smarter. >> Well, let's get into that. What are the top applications? What are you seeing with InfluxDB, the time series, what's the sweet spot for the application use case and some customers? Give some examples. >> Yeah so it's pretty easy to understand on one side of the equation, that's the physical side is, sensors are getting cheap obviously we know that. The whole physical world is getting instrumented, your home, your car, the factory floor, your wrist watch, your healthcare, you name it, it's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but they're all on that side, they're all about IOT. So they're thinking about consumer IOT kind of projects like Google's Nest, Tudor, particle sensors, even delivery engines like Rappi, who deliver the instant car to South America. Like anywhere there's a physical location and that's on the consumer side. And then another exciting space is the industrial side. Factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational. Because what has to get smarter when you're building a factory is systems all have to get smarter. And then lastly, a lot in the renewables, so sustainability. So a lot, you know, Tesla, Lucid motors, Nicola motors, you know, lots to do with electric cars, solar arrays, windmills arrays, just anything that's going to get instrumented that where that instrumentation becomes part of what the purpose is. >> It's interesting the convergence of physical and digital is happening with the data. IOT you mentioned, you know, you think of IOT, look at the use cases there. It was proprietary OT systems, now becoming more IP enabled, internet protocol. And now edge compute, getting smaller, faster, cheaper. AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? Where's the IOT OT dots connecting to? Because, you know as these two cultures merge, operations basically, industrial, factory, car, they got to get smarter. Intelligent edge is a buzzword but I mean, it has to be more intelligent. Where's the 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 action's really happen at the developer. And so what you're seeing is a movement in the world that maybe you and I grew up in with IT or OT moving increasingly that developer driven capability. And so all of these IOT systems, they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business? What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express themselves? Or am I trying to figure out when the next heart rate monitor is going to show up in my apple watch? Right, what am I trying to do? What's the system I need to build? And so starting with the developer is where all of the good stuff happens here. Which is different than it used to be, right. It used to be you'd buy an application or a service or a SaaS thing for, but with this dynamic, with this integration of systems, it's all about bespoke, it's all about building something. >> So let's get to the developer real quick. Real highlight point here is the data, I mean, I could see a developer saying, "Okay, I need to have an application for the edge," IOT edge or car, I mean we're going to have, I mean Tesla got applications of the car, it's right there. I mean, there's the modern application life cycle now. So take us through how does this impacts the developer. Does it impact their CICD pipeline? Is it cloud native? I mean where does this go to? >> Well, so first of all you're talking about, there was an internal journey that we had to go through as a company which I think is fascinating for anybody that'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 CICD environment. So to 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 a degree that that service is cloud. Then increasingly we remove from an agile development to a CICD environment, which you're shipping code to production every day. And so it's not just the developers, it's all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also going to happen in a big way. >> When your customer base that you have now, and as you see evolving with in InfluxDB, is it that they're going to be writing more of the application or relying more on others? I mean obviously it's an open source component here. So when you bring in kind of old way, new way, old way was, I got a proprietary platform running all this IOT stuff, and I got to write, here's an application that's general purpose. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does this job. >> A good way to think about this is- >> Versus new way which is what? >> So yeah a good way to think about this is what's the role of the developer/architect, CTO, that chain within a large, with an enterprise or a company. And so the way to think about is I started my career in the aerospace industry. And so when you look at what Boeing does to assemble a plane, they build very very few of the parts. Instead what they do is they assemble. They buy the wings, they buy the engines, they assemble, actually they don't buy the wings. That's the one thing, they buy the material for the wing. They build the wings 'cause there's a lot of tech in the wings, and they end up being assemblers, smart assemblers of what ends up being a flying airplane. Which is a pretty big deals even now. And so what happens with software people is, they have the ability to pull from you know, the best of the open source world. So they would pull a time series capability from us, then they would assemble that with potentially some ETL logic from somebody else. Or they'd assemble it with a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers but they become masters of that bespoke application. And I think that's where it goes 'cause you're not writing native code for everything. >> So they're more flexible, they have faster time to market 'cause they're assembling. >> Way faster. >> And they get to still maintain their core competency, AKA 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 that this idea of a systems developing, a system architecture. I mean systems have consequences when you make changes. So when you have now cloud data center on-premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >> That's exactly, but that's where that Boeing or that airplane building analogy comes in. For us, we've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on prem edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that, you want to 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. >> Does that mean you have a PO for- >> (laughs) A big check, a blank check, if you can answer this question. >> Only if in tech. >> If you get the question right. I got all this important operation stuff, I got my factory, I got my self-driving cars, this isn't like trivial stuff, this is my business. How should I be thinking about time series? Because now I have to make these architectural decisions as you mentioned and it's going to impact my application development. So huge decision point for your customers. What should I care about the most? What's in it for me? Why is time series important? >> Yeah, that's a great question. So chances are, if you've got a business that was 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that, you built something on Oracle, or you built something on IBM's Db2, right, and you made it work within your system. Right, and so that's what you started building. So it's already out there, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time, I hate the word, but digital transformation. Then you start with time series, it's a foundational base layer for any system that you're going to build. There's no system I can think of where time series shouldn't be the foundational base layer. If you just want to store your data and just leave it there and then maybe look it up every five years, that's fine. That's not time 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's the value to me when I implement this? What's the end state? What's it look like when it's up and running? What's the value proposition for me? What's in it for me? >> So when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, the transforming it in near real time. So that the other dependencies that a system it gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's doing every action that's above, but it's foundational to build a really compelling intelligence system. I think that's what developers and architects are seeing now. >> Bottom line, final word, what's in it for the customer? What's your statement to the customer? What would you say to someone looking to do something in time series and edge? >> Yeah so it's pretty clear to us that if you're building, if you view yourself as being in the business of building systems, that you want '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 want to say what's in it for us, Influx. What's in it for us is, people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare, it's hard not to be proud or feel like, "Wow." >> 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 also exciting on our side of the equation. >> Yeah, it's critical infrastructure, critical of operations. >> Yeah. >> Great stuff. Evan thanks for coming on, appreciate this segment. All right, in a moment, Brian Gilmore director of IOT and emerging technology at InfluxData will join me. You're watching theCUBE, leader in tech coverage. Thanks for watching. (upbeat music)

(intense orchestral music) >> Hey welcome back everybody, Jeff Frick here with theCUBE We are taking a short break from the madness of the conference season to do some CUBE Conversations here in the Palo Alto studio, which we always like to do and meet new people, and hear new stories, learn about new companies. And today we've got a new company, we've never had 'em on theCUBE before, it's Evan Kaplan, he's the CEO of InluxData. Evan, great to see you. >> Yeah, hey thanks for having me. >> Absolutely. So for people that aren't familiar with the company, give 'em kind of the 101 on Influx. >> Yeah so, InfluxData is an opensource platform for collecting metrics and events at scale. The company is about almost four years old, has a large selection of tier one customers, is broadly accepted by developers as the number one time-series platform out there, so. >> So a lot of people talk about collecting data, so we've been doing Splunk since 2012, and, they really found something interesting on log files, and took it a whole 'nother level, so there's a lot of people that are capturing events. So what do you guys do that's a little bit different, how are you slicing and dicing this opportunity? >> Yeah, to put this is even in the broader context of what we're looking at is the 20 year break-up of the Oracle, DB2 and Formex franchise that dominated and relational databases were the answer to all problems and so if you look at a company like Splunk working on logs, they optimized a platform for those logs, for that data set, Elastic also, really interesting space. I think our innovation has been in saying "Hey, where the world's going, where all of these complex systems are going?" Particularly IoT, is to real-time view of the data and so, rather than collect verbose logs, historical views of the data and things like that, real system operators, real developers and builders want to instrument their applications, their infrastructure, so you can view 'em in real time. The place where the rubber hits the road is IoT. Sensors spit out metrics and events, period, full stop. And so if you want to be performant in how you handle, your instrumentation of the physical world, and how you do your machine learning, and how you want to manage these systems, you use a fundamentally time-series based database. As opposed to Splunk or Elastic or, which are primarily search-based databases. >> And are you primarily capturing and standardizing the data to feed other analytics tools, or do you have the whole suite, where you're doing some of the analytics as well? >> Yeah, such a great question. So, the fundamental platform is called the TICK Stack, and it stands for Telegraf which is a collector, which has about 200 different collectors that sit out there in the world and collect everything from SNMP data, to Oracle data, to application, to micro-service data, to Kubernetes, to that sort of stuff. There's Influx, which is the DB, which is highly optimized for millions and millions of writes a second, so collecting data points and samples. There's Chronograf which is the visualization engine and so, it allows you as soon as the data comes input you can see how it's graphed, see it on time-series oriented graphing, and then there's Kapacitor which takes action on the data. What we don't do is the super high sophisticated analytics. There are lots of companies in Silicon Valley who take our data, pump it up, and then we put it back on the platform to build a control loop for it. >> Right. So when the Kapacitor, does your application then take action on those things? >> Yes. Yeah, so, it'd do everything from alerting, to sending out another machine request, to spinning up a new Kubernetes pod, to basically scaling the application, self healing. >> Right. So does it fit in between a lot of those other types of applications that are sending off notifications, and those types of things? >> Yes, yeah. so you're in between? >> And usually, we're instrumented the way a standard developer, or an architect or CTO does is they look at a complex application, or a complex set of sensors, they instrument with Influx and Telegraf, and collect that data, they view it in real time, and then they build control loops, automation loops, to make that easier so when you see a problem, it's got a tolerance you can self adjust for. So it's the beginning of kind of the self-healing system. >> Okay, and I know that Telegraf is definitely opensource, are the other three? >> All four are open-source All four are open-source. >> Everything, in our world, everything for a developer is free, so, and a single note of Influx can handle a couple million writes a second, which is really really performant to run in production. Where our business model is, where we make money is, our closed source clustering, sharding, distributing the database, if you decide you want to run highly available in the production environment, you would buy our closed-source stuff. We have about 430 customers who run our closed source stuff on top of the opensource. >> So, it is kind of like a MapR to Hadoop if you will, where, you know, it's built on, built on the opensource, and then they've got their proprietary stuff kind of wrapped around it, almost like an open core? Or is that a? >> Yeah, it's a little It's a little different than the normal Hadoop stuff. One is, our stuff doesn't have any external dependencies. It can work with other third party projects, but just, it's a platform onto itself, there aren't 25 projects. There are four different projects, we own them all, they come across as a single binary, and it's not part of Apache. >> So they're integrated So the TICK is the full TICK >> Yes, and then you put the clustering on top. So there's some similarity, but not being part of Apache, we can control and keep clean what that experience is. And we're about, the thing that's been most successful for us is, well Paul our founder who is my partner, it's called time to awesome, the idea that a developer in 10 minutes can very quickly be up and instrumenting an application or a set of sensors, and see that data pouring in within 10 minutes from going to the site and downloading the opensource. >> So it's interesting, the giant opportunity is really around IoT, just in terms of the explosion of the sensor data, and we see that coming, and we were at AT&T show a couple weeks ago, talking about 5G which is, slowly, slowly coming down the road, (Evan laughs) they've got the standards fixed. But in terms of the, you said the shorter term, nobody has budget, I always like to joke, nobody has budget for a new platform, they do have budget for new applications, because they've got real problems. So you said you're seeing, your main success now, your go to market application, is around application monitoring? Would that be accurate, or what is kind of your? >> Yeah, there are two broad things, and they're both very similar technology as a service. One is the central monitoring stuff so, Tesla's Power Wall, Seimens' Windmills, a variety of solar companies build Telegraf into their platforms and then use InluxData to collect and store that information and analyze it. On the software side, people like IBM's Cloud Service running their network and their fabric, SAP with Ariba, Cisco with all their collaboration stuff, they instrument their software applications. And that's the idea is it's a general purpose platform for collecting and instrumenting instrumenting the applications or the sensors, either one, or both. >> Okay, and so what are you guys working on now, what's next, kind of raise the profile, get some new stuff >> Yeah, so we are-- before the whole IoT thing completely explodes, we're not quite there yet but it's coming down the pike. >> But we're starting to see it really happen, so that's really exciting for us. And this is just a really, really big market, it's certainly a super set of the log market, it should be. As you think about just the instrumentation of the physical world, how much instrumentation is going on, your clothes, your cars, your homes, your industrial devices, my watch, how much sensor data there is. We think this is a tremendously large market, so we're doing a couple of things. One is, we're about to introduce a new language for querying these kinds of time-series data that's going to be opensource, that a bunch of other people can use with their data stores. We're rolling out a new API-driven service, so that people can store these things directly in the could natively, so all they have to do is know our API. So we're really trying to push from the technology limit we're a product-driven company, and so, and an opensource-driven company, so we're trying to push that, that community is super important to us. >> It's so wild to me, the opportunity to have a closed feedback loop between someone's product back to the barn, you're barely starting to see it, Tesla obviously, is a good example, they're slowly seeing it in other places. But what a fundamental change in manufacturing, from building a product, making some assumptions about use, shipping that product to your distribution, and then, maybe you get some feedback now an then, versus actually monitoring the way that that thing is actually used by your end user, whether it's a product like a car, or even a software application, as you're rolling out all these different apps and features in the apps, how are people using it, are they using it? Where do you double down, where do you back off? And that loop has not really been >> That's pretty insightful. >> opened up very wide. Yeah, no it's just starting to open up, and that whole notion of product telemetry, my prediction is is that, as development teams grow and things like that, you're going to have telemetry experts, people are going to be specializing. How do you instrument these products so you get maximum engagement, and usage, and things like that? So I think that's pretty insightful on your part. If you think about it from a systems point of view, right? Instrumentation is first. You can't do anything 'til you instrument, whether it's telemetry from a product, it's the engagement or this. So instrumentation is first, visibility in real time is second. So observability is the big thought in systems application and building now, this notion of observing your system in real time, because you don't know, apriori, it's impossible to know a complex system, how it's going to behave, then it's automation, right? So like, okay now I can see these behaviors, how do I automate something that makes the experience for you, the user, better? But lastly, we can see this with self-driving cars, it's autonomy. It's the idea that the system becomes self-healing, and AI, and those sorts of things, but that's kind of the last step. There's a lot of learning in that process to get there. >> And it has to be automated because at scale there's no way for people to keep up with this stuff, and then how do you separate signal from noise and how do you know what to do? So you've got to automate a whole bunch of this. >> And you know if we had an aspiration it would be we're not going to write the applications that do these things but what we want to do is be that system of record so that people have a really efficient, effective metrics and events store so they can really track and keep track of all that engagement. Time-stamped data, for lack of a better way to say it. >> It sounds like you're in a pretty good space, Evan. >> Pretty excited (chuckles), thank you. Thanks for saying that, but yeah, we're pretty excited. >> Alright, well thanks for taking a few minutes out of your day and sharing the story, we look forward to watching the journey. >> Yeah. Thanks man. Alright, take care. >> Alright, thanks. He's Evan, I'm Jeff, you're watching theCUBE. We're having a CUBE Conversation in Palo Alto, we'll see you next time, thanks for watching. (intense orchestral 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.

>> Announcer: From Sand Hill Road in the heart of Silicon Valley, it's theCUBE, presenting the People First Network, insights from entrepreneurs and tech leaders. (bright electronic music) >> Everyone, welcome to this special CUBE conversation. We're here at Sand Hill Road at Mayfield Fund. This is theCUBE, co-creation of the People First Network content series. I'm John Furrier, host of theCUBE. Our next guest, Yuvi Kochar, who's the Data-centric Digital Transformation Strategist at GameStop. Variety of stints in the industry, going in cutting-edge problems around data, Washington Post, comScore, among others. You've got your own practice. From Washington, DC, thanks for joining us. >> Thank you, thanks for hosting me. >> This is a awesome conversation. We were just talking before we came on camera about data and the roles you've had over your career have been very interesting, and this seems to be the theme for some of the innovators that I've been interviewing and were on the People First is they see an advantage with technology, and they help companies, they grow companies, and they assist. You did a lot of different things, most notably that I recognized was the Washington Post, which is on the mainstream conversations now as a rebooted media company with a storied, historic experience from the Graham family. Jeff Bezos purchased them for a song, with my opinion, and now growing still, with the monetization, with subscriber base growing. I think they're number one in subscribers, I don't believe, I believe so. Interesting time for media and data. You've been there for what, how many years were you at the Washington Post? >> I spent about 13 years in the corporate office. So the Washington Post company was a conglomerate. They'd owned a lot of businesses. Not very well known to have owned Kaplan, education company. We owned Slate, we owned Newsweek, we owned TV stations and now they're into buying all kinds of stuff. So I was involved with a lot of varied businesses, but obviously, we were in the same building with the Washington Post, and I had front row seat to see the digital transformation of the media industry. >> John: Yeah, we-- >> And how we responded. >> Yeah, I want to dig into that because I think that illustrates kind of a lot what's happening now, we're seeing with cloud computing. Obviously, Cloud 1.0 and the rise of Amazon public cloud. Clearly, check, done that, a lot of companies, startups go there. Why would you provision a data center? You're a startup, you're crazy, but at some point, you can have a data center. Now, hybrid cloud's important. Devops, the application development market, building your own stack, is shifting now. It seems like the old days, but upside down. It's flipped around, where applications are in charge, data's critical for the application, infrastructure's now elastic. Unlike the old days of here's your infrastructure. You're limited to what you can run on it based on the infrastructure. >> Right. >> What's your thoughts on that? >> My thoughts are that, I'm a very, as my title suggests, data-centric person. So I think about everything data first. We were in a time when cloud-first is becoming old, and we are now moving into data-first because what's happening in the marketplace is the ability, the capability, of data analytics has reached a point where prediction, in any aspect of a business, has become really inexpensive. So empowering employees with prediction machines, whether you call them bots, or you call them analytics, or you call them machine learning, or AI, has become really inexpensive, and so I'm thinking more of applications, which are built data-out instead of data-in, which is you build process and you capture data, and then you decide, oh, maybe I should build some reporting. That's what we used to do. Now, you need to start with what's the data I have got? What's the data I need? What's the data I can get? We were just talking about, everybody needs a data monetization strategy. People don't realize how much asset is sitting in their data and where to monetize it and how to use it. >> It's interesting. I mean, I got my computer science degree in the 80s and one of the tracks I got a degree in was database, and let's just say that my main one was operating system. Database was kind of the throwaway at that time. It wasn't considered a big field. Database wasn't sexy at all. It was like, database, like. Now, if you're a database, you're a data guru, you're a rock star. The world has changed, but also databases are changing. It used to be one centralized database rules the world. Oracle made a lot of money with that, bought all their competitors. Now you have open source came into the realm, so the world of data is also limited by where the data's stored, how the data is retrieved, how the data moves around the network. This is a new dynamic. How do you look at that because, again, lagging in business has a lot to do with the data, whether it's in an application, that's one thing, but also having data available, not necessarily in real time, but if I'm going to work on something, I want the data set handy, which means I can download it or maybe get real-time. What's your thoughts on data as an element in all that moving around? >> So I think what you're talking about is still data analytics. How do I get insights about my business? How do I make decisions using data in a better way? What flexibility do I need? So you talk about open source, you think about MongoDB and those kind of databases. They give you a lot of flexibility. You can develop interesting insights very quickly, but I think that is still very much thinking about data in an old-school kind of way. I think what's happening now is we're teaching algorithms with data. So data is actually the software, right? So you get an open source algorithm. I mean Google and everybody else is happy to open source their algorithms. They're all available for free. But what, the asset is now the data, which means how you train your algorithm with your data, and then now, moving towards deploying it on the edge, which is you take an algorithm, you train it, then you deploy it on the edge in an IoT kind of environment, and now you're doing decision-making, whether it's self-driving cars, I mean those are great examples, but I think it's going down into very interesting spaces in enterprise, which is, so we have to all think about software differently because, actually, data is a software. >> That's an interesting take on it, and I love that. I mean I wrote a blog post in 2007 when we first started playing with the, in looking at the network effects on social media and those platforms was, I wrote a post, it was called Data is the New Development Kit. Development kit was what people did back then. They had a development kit and they would download stuff and then code, but the idea was is that data has to be part of the runtime and the compilation of, as software acts, data needs to be resident, not just here's a database, access it, pull it out, use it, present it, where data is much more of a key ingredient into the development. Is that kind of what you're getting at? >> Yes. >> Notion of-- >> And I think we're moving from the age of arithmetic-based machines, which is we put arithmetic onto chips, and we then made general-purpose chips, which were used to solve a huge amount of problems in the world. We're talking about, now, prediction machines on a chip, so you think about algorithms that are trained using data, which are going to be available on chips. And now you can do very interesting algorithmic work right on the edge devices, and so I think a lot of businesses, and I've seen that recently at GameStop, I think business leaders have a hard time understanding the change because we have moved from process-centric, process automation, how can I do it better? How can I be more productive? How can I make better decisions? We have trained our business partners on that kind of thinking, and now we are starting to say, no, no, no, we've got something that's going to help you make those decisions. >> It's interesting, you mentioned GameStop. Obviously, well-known, my sons are all gamers. I used to be a gamer back before I had kids, but then, can't keep up anymore. Got to be on that for so long, but GameStop was a retail giant in gaming. Okay, when they had physical displays, but now, with online, they're under pressure, and I had interviewed, again, at an Amazon event, this Best Buy CIO, and he says, "We don't compete with price anymore. "If they want to buy from Amazon, no problem, "but our store traffic is off the charts. "We personalize 50,000 emails a day." So personalization became their strategy, it was a data strategy. This is a user experience, not a purchase decision. Is this how you guys are thinking about it at GameStop? >> I think retail, if you look at the segment per se, personalization, Amazon obviously led the way, but it's obvious that personalization is key to attract the customer. If I don't know what games you play, or if I don't know what video you watched a little while ago, about which game, then I'm not offering you the product that you are most prone or are looking for or what you want to buy, and I think that's why personalization is key. I think that's-- >> John: And data drives that, and data drives that. >> Data drives that, and for personalization, if you look at retail, there's customer information. You need to know the customer. You need to know, understand the customer preferences, but then there's the product, and you need to marry the two. And that's where personalization comes into play. >> So I'll get your thoughts. You have, obviously, a great perspective on how tech has been built and now working on some real cutting-edge, clear view on what the future looks like. Totally agree with you, by the way, on the data. There's kind of an old guard/new guard, kind of two sides of the street, the winners and the losers, but hey, look, I think the old guard, if they don't innovate and become fresh and new and adopt the modern things that need to attract the new expectations and new experiences from their customers, are going to die. That being said, what is the success formula, because some people might say, hey, I'm data-driven. I'm doing it, look at me, I'm data. Well, not really. Well, how do you tell if someone's really data-driven or data-centric? What's the difference? Is there a tell sign? >> I think when you say the old guard, you're talking about companies that have large assets, that have been very successful in a business model that maybe they even innovated, like GameStop came up with pre-owned games, and for the longest of times, we've made huge amount of revenue and profit from that segment of our business. So yes, that's becoming old now, but I think the most important thing for large enterprises at least, to battle the incumbent, the new upstarts, is to develop strategies which are leveraging the new technologies, but are building on their existing capability, and that's what I drive at GameStop. >> And also the startups too, that they were here in a venture capital firm, we're at Mayfield Fund, doing this program, startups want to come and take a big market down, or come in on a narrow entry and get a position and then eat away at an incumbent. They could do it fast if they're data-centric. >> And I think it's speed is what you're talking about. I think the biggest challenge large companies have is an ability to to play the field at the speed of the new upstarts and the firms that Mayfield and others are investing in. That's the big challenge because you see this, you see an opportunity, but you're, and I saw that at the Washington Post. Everybody went to meetings and said, yes, we need to be digital, but they went-- >> They were talking. >> They went back to their desk and they had to print a paper, and so yes, so we'll be digital tomorrow, and that's very hard because, finally, the paper had to come out. >> Let's take us through the journey. You were the CTO, VP of Technology, Graham Holdings, Washington Post, they sold it to Jeff Bezos, well-documented, historic moment, but what a storied company, Washington Post, local paper, was the movie about it, all the historic things they've done from a reporting and journalism standpoint. We admire that. Then they hit, the media business starts changing, gets bloated, not making any money, online classifieds are dying, search engine marketing is growing, they have to adjust. You were there. What was the big, take us through that journey. >> I think the transformation was occurring really fast. The new opportunities were coming up fast. We were one of the first companies to set up a website, but we were not allowed to use the brand on the website because there was a lot of concern in the newsroom that we are going to use or put the brand on this misunderstood, nearly misunderstood opportunity. So I think it started there, and then-- >> John: This is classic old guard mentality. >> Yes, and it continued down because people had seen downturns. It's not like media companies hadn't been through downturns. They had, because the market crashes and we have a recession and there's a downturn, but it always came back because-- >> But this was a wave. I mean the thing is, downturns are economic and there's business that happens there, advertisers, consumption changes. This was a shift in their user base based upon a technology wave, and they didn't see it coming. >> And they hadn't ever experienced it. So they were experiencing it as it was happening, and I think it's very hard to respond to a transformation of that kind in a very old-- >> As a leader, how did you handle that? Give us an example of what you did, how you make your mark, how do you get them to move? What were some of the things that were notable moments? >> I think the main thing that happened there was that we spun out washingtonpost.com. So it became an independent business. It was actually running across the river. It moved out of the corporate offices. It went to a separate place. >> The renegades. >> And they were given-- >> John: Like Steve Jobs and the Macintosh team, they go into separate building. >> And we were given, I was the CTO of the dotcom for some time while we were turning over our CTO there, and we were given a lot of flexibility. We were not held accountable to the same level. We used the, obviously, we used-- >> John: You were running fast and loose. >> And we were, yes, we had a lot of flexibility and we were doing things differently. We were giving away the content in some way. On the online side, there was no pay wall. We started with a pay wall, but advertising kind of was so much more lucrative in the beginning, that the pay wall was shut down, and so I think we experimented a lot, and I think where we missed, and a lot of large companies miss, is that you need to leave your existing business behind and scale your new business, and I think that's very hard to do, which is, okay, we're going to, it's happening at GameStop. We're no longer completely have a control of the market where we are the primary source of where, you talk about your kids, where they go to get their games. They can get the games online and I think-- >> It's interesting, people are afraid to let go because they're so used to operating their business, and now it has to pivot to a new operating model and grow. Two different dynamics, growth, operation, operating and growing. Not all managers have that growth mindset. >> And I think there's also an experience thing. So most people who are in these businesses, who've been running these businesses very successfully, have not been watching what's happening in technology. And so the technology team comes out and says, look, let me show you what we can do. I think there has to be this open and very, very candid discussion around how we are going to transform-- >> How would you talk about your peer, developed peers out there, your peers and other CIOs, and even CISOs on the security side, have been dealing with the same suppliers over, and in fact, on the security side, the supplier base is getting larger. There's more tools coming out. I mean who wants another tool? So platform, tool, these are big decisions being made around companies, that if you want to be data-centric, you want to be a data-centric model, you got to understand platforms, not just buying tools. If you buy a hammer, they will look like a nail, and you have so many hammers, what version, so platform discussions come in. What's your thoughts on this? Because this is a cutting-edge topic we've been talking about with a lot of senior engineering leaders around Platform 2.0 coming, not like a classic platform to... >> Right, I think that each organization has to leverage or build their, our stack on top of commodity platforms. You talked about AWS or Azure or whatever cloud you use, and you take all their platform capability and services that they offer, but then on top of that, you structure your own platform with your vertical capabilities, which become your differentiators, which is what you take to market. You enable those for all your product lines, so that now you are building capability, which is a layer on top of, and the commodity platforms will continue to bite into your platform because they will start offering capabilities that earlier, I remember, I started at this company called BrassRing, recruitment automation. One of the first software-as-a-service companies, and I, we bought a little company, and the CTO there had built a web server. It was called, it was his name, it was called Barrett's Engine. (chuckles) And so-- >> Probably Apache with something built around it. >> So, in those days, we used to build our own web servers. But now today, you can't even find an engineer who will build a web server. >> I mean the web stack and these notions of just simple Web 1.0 building blocks of change. We've been calling it Cloud 2.0, and I want to get your thoughts on this because one of the things I've been riffing on lately is this, I remember Marc Andreessen wrote the famous article in Wall Street Journal, Software is Eating the World, which I agree with in general, no debate there, but also the 10x Engineer, you go into any forum online, talking about 10x Engineers, you get five different opinions, meaning, a 10x Engineer's an engineer who can do 10 times more work than an old school, old classical engineer. I bring this up because the notion of full stack developer used to be a real premium, but what you're talking about here with cloud is a horizontally scalable commodity layer with differentiation at the application level. That's not full stack, that's half stack. So you think the world's kind of changing. If you're going to be data-centric, the control plane is data. The software that's domain-specific is on top. That's what you're essentially letting out. >> That's what I'm talking about, but I think that also, what I'm beginning to find, and we've been working on a couple of projects, is you put the data scientists in the same room with engineers who write code, write software, and it's fascinating to see them communicate and collaborate. They do not talk the same language at all. >> John: What's it like? Give us a mental picture. >> So a data scientist-- >> Are they throwing rocks at each other? >> Well, nearly, because the data scientists come from the math side of the house. They're very math-oriented, they're very algorithm-oriented. Mathematical algorithms, whereas software engineers are much more logic-oriented, and they're thinking about scalability and a whole lot of other things, and if you think about, a data scientist develops an algorithm, it rarely scales. You have to actually then hand it to an engineer to rewrite it in a scalable form. >> I want to ask you a question on that. This is why I got you and you're an awesome guest. Thanks for your insights here, and we'll take a detour into machine learning. Machine learning really is what AI is about. AI is really nothing more than just, I love AI, it gets people excited about computer science, which is great. I mean my kids talk about AI, they don't talk about IoT, which is good that AI does that, but it's really machine learning. So there's two schools of thought on machine. I call it the Berkeley school on one end, not Berkeley per se but Berkeley talks about math, machine learning, math, math, math, and then you have other schools of thought that are on cognition, that machine learning should be more cognitive, less math-driven, spectrum of full math, full cognition, and everything in between. What's your thoughts on the relationship between math and cognition? >> Yeah, so it's interesting. You get gray hair and you kind of move up the stack, and I'm much more business-focused. These are tools. You can get passionate about either school of thought, but I think that what that does is you lose sight of what the business needs, and I think it's most important to start with what are we here trying to do, and what is the best tool? What is the approach that we should utilize to meet that need? Like the other day, we were looking at product data from GameStop, and we know that the quality of data should be better, but we found a simple algorithm that we could utilize to create product affinity. Now whether it's cognition or math, it doesn't matter. >> John: The outcome's the outcome. >> The outcome is the outcome, and so-- >> They're not mutually exclusive, and that's a good conversation debate but it really gets to your point of does it really matter as long as it's accurate and the data drives that, and this is where I think data is interesting. If you look at folks who are thinking about data, back to the cloud as an example, it's only good as what you can get access to, and cybersecurity, the transparency issue around sharing data becomes a big thing. Having access to the data's super important. How do you view that for, as CIOs, and start to think about they're re-architecting their organizations for these digital transformations. Is there a school of thought there? >> Yes, so I think data is now getting consolidated. For the longest time, we were building data warehouses, departmental data warehouses. You can go do your own analytics and just take your data and add whatever else you want to do, and so the part of data that's interesting to you becomes much more clean, much more reliable, but the rest, you don't care much about. I think given the new technologies that are available and the opportunity of the data, data is coming back together, and it's being put into a single place. >> (mumbles) Well, that's certainly a honeypot for a hacker, but we'll get that in a second. If you and I were doing a startup, we say, hey, let's, we've got a great idea, we're going to build something. How would we want to think about the data in terms of having data be a competitive advantage, being native into the architecture of the system. I'll say we use cloud unless we need some scale on premise for privacy reasons or whatever, but we would, how would we go to market, and we have an app, as apps defined, great use case, but I want to have extensibility around the data, I don't want to foreclose any future options, How should I think about my, how should we think about our data strategy? >> Yes, so there was a very interesting conversation I had just a month ago with a friend of mine who's working at a startup in New York, and they're going to build a solution, take it to market, and he said, "I want to try it only in a small market "and learn from it," and he's going very old school, focus groups, analytics, analysis, and I sat down, we sat at Grand Central Station, and we talked about how, today, he should be thinking about capturing the data and letting the data tell him what's working and what's not working, instead of trying to find focus groups and find very small data points to make big decisions. He should actually utilize the target, the POC market, to capture data and get ready for scale because if you want to go national after having run a test in... >> Des Moines, Iowa. >> Part of New York or wherever, then you need to already have built the data capability to scale that business in today's-- >> John: Is it a SaaS business? >> No, it's a service and-- >> So he can instrument it, just watch the data. >> And yes, but he's not thinking like that because most business people are still thinking the old way, and if you look at Uber and others, they have gone global at such a rapid pace because they're very data-centric, and they scale with data, and they don't scale with just let's go to that market and then let's try-- >> Yeah, ship often, get the data, then think of it as part of the life cycle of development. Don't think it as the old school, craft, launch it, and then see how it goes and watch it fail or succeed, and know six months later what happened, know immediately. >> And if you go data-centric, then you can turn the R&D crank really fast. Learn, test and learn, test and learn, test and learn at a very rapid pace. That changes the game, and I think people are beginning to realize that data needs to be thought about as the application and the service is being developed, because the data will help scale the service really fast. >> Data comes into applications. I love your line of data is the new software. That's better than the new oil, which has been said before, but data comes into the app. You also mentioned that app throws off data. >> Yuvi: Yes. >> We know that humans have personal, data exhaust all the time. Facebook made billions of dollars on our exhaust and our data. The role of data in and out of the application, the I/O of the application, is a new concept, you brought that up. I like that and I see that happening. How should we capture that data? This used to be log files. Now you got observability, all kinds of new words kind of coming into this cloud equation. How should people think about this? >> I think that has to be part of the design of your applications, because data is application, and you need to design the application with data in mind, and that needs to be thought of upfront, and not later. >> Yuvi, what's next for you? We're here in Sand Hill Road, VC firm, they're doing a lot of investments, you've got a great project with GameStop, you're advising startups, what's going on in your world? >> Yes, so I'm totally focused, as you probably are beginning to sense, on the opportunity that data is enabling, especially in the enterprise. I'm very interested in helping business understand how to leverage data, because this is another major shift that's occurring in the marketplace. Opportunities have opened up, prediction is becoming cheap and at scale, and I think any business runs on their capability to predict, what is the shirt I should buy? How many I should buy? What color should I buy? I think data is going to drive that prediction at scale. >> This is a legit way that everyone should pay attention to. All businesses, not just one-- >> All businesses, everything, because prediction is becoming cheap and automated and granular. That means you need to be able to not just, you need to empower your people with low-level prediction that comes out of the machines. >> Data is the new software. Yuvi, thanks so much for great insight. This is theCUBE conversation. I'm John Furrier here at Sand Hill Road at the Mayfield Fund, for the People First Network series. Thanks for watching. >> Yuvi: Thank you. 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New Orleans Louisiana it that you covering dot next conference 2018 brought to you by Nutanix welcome back to the cubes coverage here of Nutanix next 2018 here in New Orleans Louisiana the brass bands are going talking to lots of customers been a great event so far and for Keith Townsend and I'm Stu minimun we always love to be able to talk to customers each one of them has a different story different analysis different challenges they're facing happened to welcome Alex Walker who's the senior vice president of IT at IBC Bank out of Texas thanks so much for joining us thank you alright so you've actually been going to all the shows just like I have at this Keys first one who's been getting getting the inaugural visit here but first of all tell us about yourself a little bit of background what you run at the bank and just give us quick sketch of the bank oh let's start with the bank you know we're in our 51st year we are based in Laredo Texas and it's a community bank mostly and commercial right you know where we are 19th according to Forbes magazine best banks in the country so we went up from 46 to 19 this year Congrats and so great accomplishments for the bank itself it's a great operation we're in 88 cities in the in Texas and Oklahoma and about 13 billion in assets great and what's under your purview as the SPID okay so you an all IT for the bank I'm not this not the development side of it you know without the operations infrastructure yes okay 51 year old company finance going through a lot of changes before we get in to the tech just give us one or two some of those the stresses and strains that you're feeling is business well regulation is one right over the last several years the increasing regulation has caused a tremendous growth in our auditing Keith Kountz rates at Department and a lot of cost to the bank I started about three and a half years ago and the bank was looking at how do we prepare for a change right potentially hoping for some change in them in the regulatory climate and so forth but we needed to prepare for that and prepare for growth so we we need to take a look at our infrastructure of everything across the board yeah maybe could organizationally where does that pension for change come from what kind of air cover do you get from from News exercise what's of the staff underneath you know how do they take change well initially there was a lot of reluctance right people were in the status quo they're comfortable with what they had not necessarily happy with it but taking on change is it's difficult right so we looked at operational costs some of the basic things we had two data centers at the time literally a hundred and fifty yards apart from each other so we said we can do we can do more right the bank's motto is we do more and so we said how do we do more for the bank for their customers improve the quality of the services the uptime and so and and reduce its cost so it's your bank so change in a bank a lot of times means going from one store to reef vendor to another storage or a vendor and that's a big deal big set of conversations you know your your your you know it we are sitting at the 19th best bank and then the fourteenth you don't get there by turning over the table part called the table you your get you get there by being steady what made you guys actually consider something as revolutionary as HCI when it came to change well when we looked at the infrastructure that we had and you know it goes back to simon Sinek start with quiet right why are we doing this I asked I asked the my department when I first started who are our competitors and they gave us the names of all the local banks and so forth and the usual suspects the large banks and I said no they're not that's the bank's customers our competitors are AWS Rackspace Microsoft is your Google Cloud it's the these are the hosting companies we host applications for our customers we're shared services for our bank once we understand who we are then we have to take a different approach because now if we're competing with them it's no wonder that the bank is starting to branch out and do their own thing right some unit I'm getting contracts with the cloud providers or with other service providers because we're too far behind we're not cost-effective we're not competitive so it doesn't mean that we want to build massive data centers everywhere but we need to have the same level of services that they provide so from a validation perspective it you start to look at the cost of hyper-converged in general I'm sorry how long you guys have been a new tennis customer three years okay so from a cost perspective as you start to look at hyper-converged how did you even begin to compare it to your existing environment well I looked I looked at the the studies that are out there particular there was an IDC study done member on 2015 that said that customers of like size all different types of customers we're getting these these benefits I said wow if I could get those that'd be really cool right so I went to the board I went to my boss the CIO and I said I think we could get this this would be a really good but then again people said we never heard of mechanics what is this our applications aren't certified with Nutanix you know so let's talk about procurements and I said well let's just do a PLC and that's bringing this in and and we'll run VMware on it which is what we were certified at the time our applications and I said let's let's look at this infrastructure this we brought in the PLC but what we did is we took Nutanix and had them talk directly to my accounting the bank's accounting department right we all know we all work for accountants eventually and so we said if we can get them to agree that if we can get these then they're gonna be behind us from the are white and TCO models right so we went through and said what are we paying for this what are we paying for that what's the hyung going rates for this let's get some samples of if we ordered this and replace it we had eleven storage frames from seven different vendors or we couldn't move data around from one storage frame to another because we had over time acquired a lot of different frames and like most places never retired them right and so all that layer of complexity what we did is we said this on this PLC let's test this out okay cut to the chase we we got the we got the numbers we were then three or two 5% of everything that came out in that study and so we bought that that that equipment immediately placed our first order which was 12 notes still want to keep it constrained so one of food in the water but I said this is a technology I'm betting on five years we'll write it off in five years we can get rid of it and move on to the next thing what happened was we were getting so such good wins we actually completed in our five-year model we completed that in roughly around two years because the acceleration based upon the benefits that we got some of the requirements that we had for change within the organization replace all equipment and so forth we were able to to accelerate that not only that we just finished upgrading our D our site which was not part of that five-year plan and just completed that and so within three years we've now are our 97% on Nutanix and we just took delivery of 24 of the Robo nodes which we're going to put out in our branch operations that'll leave us with five servers that are not Nutanix other than our for AIX system yeah Alex can you tell us what what were the key metrics that you were looking at to measure success and you did some TCO studies you actually presented here at the conference what do you recommend to your peers as to how they should be able to evaluate rolling out something like this well for one and a big one was licensing right it's far more efficient what we got for example we got we were able to take our Fibre Channel switches running about quarter of a million plus each and and get rid of those would we when we look at it the bandwidth that that's taken on the servers it's writing data to storage going through this storage controller going into this the sand and coming back that delay was substantial so much so that when we moved all of our databases into Nutanix and eliminated that infrastructure we're running 66% faster than we did on current technology on a conventional architecture did what was the business response on this did it change anything in the business what did the users say well when they'd users Jobs ran far faster than they did before when we went back and said I don't need as many Microsoft sequel licenses as they did before for consolidation fewer cores the tremendous benefits our sequel developed our sequel management team for example it takes far less time to stand up servers do migrations things like that so what's the Delta between the prediction debrief their predicted ROI and the realized our ROI you guys realize your savings much quicker wolves worldwide little surprises well the surprises were we were conservative we didn't include any soft costs those are difficult to we missed it me Steve Kaplan are all are all I got a TCO guy for Nutanix go back and forth on the soft cost don't show weak soft cost show me where I can give the money back to my accountants who we all have to report to correct right yeah so what we found is the fact that we were conservative we were getting much better benefits so again when we look at the servers we bought too many cores right so now I this good problem now I can migrate more systems I did anticipate based upon that spin so the time the technology to the financial benefits the reduction and latency allows you to stop spending money on more cores that you didn't need less latency equals better performance better performance tools more dense newness Lourdes units means less money spent so we we actually shut down one of the data centers and migrated into a single data center and it's we're running somewhere around up third a little bit more than a third of that data center so the electrical expend is down in aggregate roughly around 40% so that's real money okay you mentioned that you're also using Nutanix for disaster recovery tell us what led to that that's a newer solution from Nutanix how that experience go we're using the note annex replication for that and when we our legacy was that some of them were taped and some of it was you know migrating data you know a typical older dr type of situation we're in our testing now and that's a little bit complex because we have to protect that dr site from production but we're mirroring the the systems exactly as they are in production so when you spin it up its life right so we have to build a barrier between those systems so if we take that even then taking that into account we can get it up in hours rather than and when we say hours like a couple of hours rather than the 22 12 to 24 hours that we were before and it's 10 systems not 4 systems so roughly about a hundred servers or so minimally all right Alex look forward a little bit tell us what's on your roadmap what kind of things you're doing and if there's intersections with titanic's there we're looking at VDI for example something that we now that we've reengineering our network as well that we're looking at doing that for branch operations and security right but looking forward into AI and and blockchain and which is going to be very disruptive for financial institutions okay you mentioned blockchain so definitely need to get get your take as to what can you share either personally or from the Bakke standpoint cryptocurrency of course will I and I do pay taxes on it but realistically I'm mining with you know for video carts it's it's not it's really understand from as a chief technologist I'm I really need to understand these things you don't make appendix fluster off on the side I did ask her I could have the old data center and the when we're doing I'm doing that really effectively to better understand that but I think what we're looking at it blockchain is tremendous opportunities for many improvements in security and loss prevention and other types of things within the financial side I'm seeing a lot of big financial institutions that are getting filing for patents on block chains and they're they're bringing it up in their ten cases potentially very disruptive and very expensive and some of them are saying specifically cryptocurrency and blockchain and some of them are saying new you know new competition in the market right so we take that to kind of mean that they're they're thinking about the same things we are so keep a eye on to the future especially when it comes to something like blockchain this relatively inefficient at processing transactions how does that impact your data center strategy you guys just went down from you know huge space reduced electrical power by 40% any considerations around kind of the blood blockchain at a commercial level of use within the bank and how it might impact your strategy we're a conservative bank so would we we're having discussions about what what does that mean right what it were do we think that's might come in and it's very early for us right we've been busy you know the datacenter moves and other types of things too so we're starting to look at that and have some a few conversations about what do we think it it is we're talking to some of our our business partners and say how might we cooperate with you guys to do excuse me to use some of this blockchain technology it's a it's a different way of doing it you know when in the past we might use relational databases like sequel server or something to do something some of this work where distributed ledger might be a far easier better way to do that so it's another tool I like to say that you know video didn't kill the radiostar right yeah there's more type of radio out there than there ever was so this is another tool that we have to look and say well how does this how do we utilize this what with the right technology for the right job and we're being very cautious about that all right well Alex Walker really appreciate you sharing all the updates on IBC Bank pleasure to catch up with you and look forward to seeing you more than ten echoes in the future for Keith Townsend Thomas do minimun more coverage here at Nutanix duck necks New Orleans thanks for watching the queue [Music]

>> Announcer: Live from Orlando, Florida, it's theCUBE! Covering Grace Hopper Celebration of Women in Computing. Brought to you buy SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper conference here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Jeff Frick. We're joined by Dr. Deborah Berebichez. She is the chief data scientist at Metis, which is owned by Kaplan. Thanks so much for joining us. >> Thank you, Rebecca. Thanks for inviting me, too. >> You have had such an interesting and varied professional career. You were even a host of a lot of different science-oriented television programs. You work on initiatives to get young women into technology. But one of the things that is most impressive is that you were the first Mexican woman to ever earn her PhD in physics-- >> Deborah: In physics, at Stanford. >> From Stanford University. What an accomplishment. But talk a little bit about your path to Stanford. Tell our viewers a little bit more about your trajectory. >> It's definitely a convoluted, and not a typical path. I grew up in Mexico City in a conservative community that discouraged girls and young women from pursuing a career in the hard sciences. I was told from a very young age that physics was for geniuses, and that I had better pick a more feminine path, like communications or something else, which were great careers, but they were not the right ones for a very inquisitive mind like mine. When I confessed to my mom in high school that I loved physics and math, she said, "Don't tell the boys, "because you'll intimidate them, "and you may not be able to get married." >> Rebecca: Nonsense! >> Actually, it's funny, because that kind of overt bias is sometimes easier to combat than the one that more women experience, which is a more subtle bias. You know, that the media tells us that some things are for boys and for women. So, in my case, it was very open, and so it almost gave me more courage to try to fight against it. Anyways, so, it came time to pick what career, what BA to do in college, and I was told by the advisors in school that philosophy was a more feminine and acceptable path, but it also asked a lot of questions about the universe. So, I enrolled in a local college in Mexico City to study philosophy, but the more I tried to stifle my love for physics and math, the more that inner voice was screaming, "This is your path. "You have to do it, you have to study physics." Just like a lot of kids do their rebellious things behind their parents' back, I would go and rent from the library books about obscure physicists like Tycho Brahe, this Danish astronomer who was locked up in a tower, and I was thinking, I'll be just like him, kind of antisocial, nobody will like me, but at least I'll have my data, my numbers, to keep me company. >> Rebecca: This was your teenage rebellion, is reading about brooding philosophers? >> Well, there other-- >> Okay. >> In the middle of my BA in philosophy in Mexico, I decided to apply to universities in the US to give it a chance, and give myself the opportunity to pursue both BAs, physics and philosophy. I was very fortunate to get a full scholarship to attend Brandeis University, and I say that because, in Mexico, universities are about eight times less expensive than in the US, so I could have not afford to go anywhere else. While at Brandeis, I took the courage to take a very general course in astronomy. Very little math, introductory course, and there I met the teaching assistant, who was a graduate student by the name of Roopesh. He was from India. Roopesh and I became good friends, and he told me that I wasn't the typical student that just wanted to get an A in the class and do the homework well, that my curiosity had no end. That I would ask questions about quantum mechanics and statistical mechanics, and I wanted to know everything about the universe and nature. So, one time, we were walking in Harvard Square, and I realized that I was the only one who could make my dream of becoming a physicist happen. With teary eyes, I told Roopesh, "I don't want to die without trying. "I just don't want to die without trying to do physics." He called his advisor on a payphone. He was the head of the graduate student committee, so he called me to Brandeis. He handed me a book called Div, Grad and Curl, Vector Calculus in Three Dimensions. For me, it was an alien language. He said to me, "There's a problem, "because the BA in physics takes four years, "and your scholarship is only for two years. "But guess what, someone else has done this at Brandeis. "His name is Ed Witten. "Do you know who he is? "He switched from history to physics." I said, "You're kidding. "Ed Witten is a very famous physicist, "the father of string theory. "Clearly, there's no way I could pull this off." He says to me, "I give you two months this summer. "If, by the end of the summer, "you pass a test on this one book, "I'll let you skip through "the first two years of the physics major "so you can complete the BA in only two years." Roopesh decided to mentor me and tutor me 10 hours a day for eight weeks. I tell the story of Roopesh because I always wanted to pay him back. He said to me, when he was growing up in India, in Darjeeling, there was an old man who would teach him and his sisters the tabla, the musical instrument, English, and math. And when they wanted to pay him back, the old man said, "No, the only way you could ever pay me back "is if you do this with someone else in the world." That's how my mission in life started, to inspire, encourage, and help other, especially women, but minorities who, like myself, want a career in STEM, but for some reason, whether it be financial or social, feel that they cannot achieve their dreams. >> Great story. >> Yeah, wow! Incredible! >> And then, you asked about Stanford. So, then I went back to Mexico, and I was doing a Master's in theoretical physics, and I was again told by my community, "Okay, you've got it over with. "Stay here, get married and stay as part of the community." But I was still more hungry for knowledge, and to do more physics. I was very late in the application cycle, and I decided to apply to schools. I went to my Mexican advisor's office, and I said, "You know, I'm going to leave again. "I'd like to go to the US where I can pursue experiments. "I wrote to a couple of professors." He says, "Who did you write to?" I say, "Well, there's one particularly interesting one, "Steve Chu at Stanford." His jaw dropped. He said, "Steve Chu?" I said, "Yes, why?" He said, "Do you realize he just won the Nobel Prize "a couple of months ago?" And Steve Chu later became Secretary of Energy in the US. I was so fortunate that he received my email with interest, invited me to work directly with him at Stanford. That's how my career started. >> It's such a good mix of fortuitousness, serendipity, but also doggedness on your part, so, really, there's a lot going on. >> Don't be shy, is my-- >> This gets to our final question, really, which is, what's your advice for the younger versions of you? >> The first thing is that it was not all easy for me. There was a lot of failure along the way. My first advice is, the people who get to the end of the line and succeed in life are not the ones that simply persevere and get everything right. They're the ones that keep getting up and succeeding step after step. It's the courage to get to the end and persevere even when failure exists. The second piece of advice, especially for parents out there, is when your kids ask questions about the world and nature, don't just give them the answer. Go through the pleasure of finding things out, as Feynman would say. Especially with computing. Computers are a tool, a magnificent tool. But they're just a tool to another goal, which is to gain insights about the world. It's more important to be a critical thinker and a thought leader, rather than just focus on being proficient at coding. >> You had the element of humor, you had the element of storytelling, you had the element of everyday things in the way, 'cause you're obviously a super smart lady to accomplish these things. Not everybody's so super smart, so you've created a style in which you can help those that aren't maybe necessarily PhDs from Stanford to gain interest, to become interested, to kind of hook 'em into this interesting world that you're so passionate about. >> Yeah, thank you. I try to do it through my TV show that I cohost with The Science Channel called Outrageous Acts of Science, which serves exactly that purpose, to get people interested in the fact that science and STEM is behind everyday life. It's not just some complicated equation in a board. It's what we go through every day, and if you just gain the joy of discovering those concepts, you're set. >> Great. Well, Deborah, thank you so much for joining us. It's been so much fun talking to you. >> Thank you. I loved being here. >> I'm Rebecca Knight for Jeff Frick. We will have more from Grace Hopper just after this. (fast techno music)