>>Okay, welcome back everyone. To the cubes coverage of AWS summit, 2022 in San Francisco, California. We're here, live on the floor at the Mosconi south events are back. I'm John fur, your host. Remember AWS summit 2022 in New York city is coming this summer. We'll be there with the live cube there as well. Look for us there, but of course, we're back in action with the cloud and AWS. Our next guest Garrett Miller is the general manager of navigation at Mapbox. I mean, it's been a Amazon customer for a long time, Garrett. Thanks for coming on the cube. >>Yeah. Thanks for having us, John. >>So you guys are in the middle of, I love the whole location base slash we developer integration. We've had many conversations on the cube around how engineers and developers are becoming embedded into the application, whether it's from a security standpoint, biometrics, all kinds of stuff, being built into the app and, and location navigation. That's right. Is huge from cars. Everyone knows their car, car map. That's right. GPS satellites, some space it's complicated. It sounds like it sounds easy, but I know it's hard. Yeah. You, you get the keynote going on today. Give us a quick update on Mapbox and we'll then we'll talk about the keynote. >>Yeah. You bet John that's right. So as you were saying, you know, it really is. It's all about location intelligence. And how does that get embedded into the applications? And to the point you made vehicles that are out there on the roads to today. So we target developers. Those are our key customers, and we've got over three and a half million registered on the platform today. They consume the modules that we build with APIs, SDKs, data sets, and more and more applications to accomplish whatever those location needs might be. >>Why we appreciate you coming on. You are featured keynote by presenter here at summit, which means Amazon thinks you're super important to share. I'll say your customer. So you, I know you've been a customer for a long time as a company, but what was your keynote about what was the main theme? The developers were all here. You got the builders. What was your content? What did you present this morning on the keynote? Yeah, >>Well, this morning we really talked a lot about logistics and the, this story that we told was know in the logistics industry, there is a massive movement to shorter and shorter delivery windows. And so the, the, the story that we told is really around a 10 minute delivery. Now, have you ever wondered how you get a 10 minute delivery? You, you place an order on your phone and all of a sudden somebody shows up at your front doorstep. You ever wonder about that? >><laugh> >>Some shows supply >>Chain. Someone's waiting in the wings from my call. >>Yeah. >>Yeah. Well, that's right. >>John's about to order sometime soon. That's right. You ready? That's right. Do all these assets. That's >>Right. They're all ready for you. But there's actually a tremendous amount that actually goes into that. And so it really starts with designing the right distribution system up front. And so we've got tools and, and applications and, and APIs that support that. And really it, every single step of the way, location is a critical aspect to making that delivery happen and getting it to a customer's doorstep in 10 minutes or less. And so how are you understanding the real time road graph that underlies a, a, a driver going from perhaps a dark store, dark kitchen to getting in, excuse me, in front of a customer in 10 minutes with hot food. >>I mean, this is a big point. I was just joking about waiting for me, you know, that, but the point is, is that it's not obvious, but it sounds really hard. I know it's hard because to have that delivery, a lot of things have to happen. It's not just knowing location. >>That's exactly >>Right. So can you just UN pull appeal back the covers on that? What's going on there? >>Yeah. So imagine this is, is, it really starts, as I was saying with designing that distribution system and it's putting in place where you might not expect it, it's actually putting in place a retail store, but these aren't any retail stores, right? These are dark stores. These are dark kitchens that are strategically placed as close as possible, the customer density, so that you can actually shorten that window as much as possible to get you whatever that order might be. But from there, it actually goes quite a bit further when an order actually comes in, you've gotta be able to understand how do I sign an, a driver to get that order to the customer in that, in that very short period of time, more often than not, it's getting it to the driver that can get there the fastest, once you've got the right driver identified, how are you actually then going to enable them to get from point a to B to get that order. >>And then perhaps from B to C to get to your front door, being able to do turn by turn navigation that reflects everything. That's how happening in the real world to be able to get there on a timely way is critical. And then wrapping around that actually the, the, the end customer's experience your experience with how are you placing that order? Yeah. How are you using Mapbox services to do that? How are you being able to track on your application and say, well, you know, great, I expect 10 minutes and they're five minutes away. Are you gonna show up our APIs and SDKs power? That experience, >>I wanna get into the product in a second, but you brought up something I think's important to highlight. One is dark kitchens, dark stores. That's right. Okay. Term people may or may not have heard of, we all have experience in COVID going to our favorite restaurant, which has been kind of downsized because of the COVID and we're waiting for our food. And someone comes in from another delivery ever standing in line next was just pick something up. I mean, they're going through the front door. That's like the, the, the branded store. So, so is it right to say that dark kitchens are essentially replicas of the store to minimize that traffic, but, but also to be efficient for something else that's right. >>It actually even goes further than that. There are many restaurant brands. Now, it only exists as a brand. They don't have a restaurant that you can go to and sit down and have that meal. They actually only operate dark kitchens to, to serve that demand of people wanting to order up, Hey, I want my food. I want it. Now, those brands exist to serve that need. >>All right. So great for the definition, we just define dark kitchens, dark stores, but also brings, I wanna get your reaction to this before we get into the product, cuz this is a trend that's right. This is not like a one off thing. That's right. It's not a pulled forward TA a market that was COVID enabled. This is actually a user experience inflection point. That's >>Right. >>Can you share your vision on what this means? Because there's mobile ordering, there's the dynamics of the kitchens as a supplier of something in stores. So that's content or a product that's being delivered to a consumer via of the web. So now there's gotta be a whole nother reef factoring of the operating environment. Now that's what's happening is that might get that >>Right? No, that's exactly right. And even if you step back, even further and you, you think about the, the journey that the logistics industry has been on, it used to be that two days was that really exciting delivery time. Right. And everybody got it super excited. Then it became next day. Then it became same day and now it's become 10 minutes. And even some companies are out there offering seven minute deliveries, right. And in order to do that, you've gotta completely retool your business. So you can think the logistics and industry really existing on two continuums, you've got pre-planned deliveries on one hand and on-demand deliveries on the other. And there are two parallel distribution systems and ecosystems and industries really springing up to serve those different types of demand. >>So you've been on Amazon web services customer for how many years, >>Since 2013 in our founding. And you know, actually we're really proud to say that we were born on Amazon and we have scaled on Amazon. >>How are they helping you scale? What are they doing to help you? >>Well, I'd say just about everything. And if you think about their, the, the services that Amazon provides for us, they power every single step of our business along the way. And so I'll give you an example. We can walk through that with some of the tech technology. I, if you think about again, how do you get 10 minutes? You gotta have a pretty precise understanding of what's going on in the real world. And so to do that, it, for us, it all starts with collecting billions and billions of data points from sensors that are out there in the world. We stream that into our technology stack, starting at the very beginning with Amazon ESIS. And so that's just the start. But then that feeds into our entire technology stack that all runs on site on top of AWS, to where we're running our own AI models that we use Amazon SageMaker to make, and then stream it back out to our AP, through our APIs, to our se Ks and applications that sit on the edge again, all leveraging Amazon technology. >>Well, I think this is a great use case and I'll get back into the, the Mapbox a second, but Amazon, you guys are executing what I call the super cloud vision, which is snowflake you guys building on their CapX leverage. You're building a super cloud on your own. It's your app, it's your cloud. >>That's right. That's right. So if you, again, if you think about it, you know, and actually just stepping back for a moment, tell about Mapbox for a second is what, what Mapbox can do is provide the most accurate digital representation of the physical world. Think about the Mapbox technology, delivering the most accurate digital twin of mother earth, right? That's what we do. And the way that we do that is to consume, as I said earlier, vast amounts of data, we've got powerful AI that structures that data, and then really robust and scalable infrastructure that underpins all of that. Now the benefit of working with a company like AWS is that they take care of that third point. Yeah. Which means we get to go focus on the first two, which is how we differentiate and build our >>Business. And that's exactly the model of how you win in the cloud. In my opinion, that's the go big piece, the go and there's tools that fit in white spaces. But that's the, I think that's the right formula. Let's get back to Mac boxer. I know you've got news. You got the, the, uh, Mapbox fleet SDK. You announced, I wanna hold on that we'll get to in a second, let's get back to what you got are providing that example as you have this new operating environment of how delivery and, and supply chain and that's example, I want to know how tech your technology is making all that work. Because I was just talking to someone last night about this web van was web 1.0 and crash never was successful. Instacart is kind of hurting. So maybe they're optimized. You could save them. I mean, cuz the economics gotta work. If you don't have the underlying system built, that might fail. So there'll be probably the third version that gets it. Right. Maybe at Mapbox again, I'm speculating, but I'll let you talk. Yeah. How does Mapbox solve the, that problem? >>You know, it's interesting if you come back to that, that, that analogy we're using with AWS and how do you use AWS to win in the cloud? It's the same story for Mapbox of how do you win in the location industry? And what we do is provide those same tool sets of APIs and SDKs, the thing go power, those logistics companies like an Instacart, who's a great customer of ours to run in their logistics business on top of it again, it's all about how do you provide technology that allows your customers yeah. To focus on what matters from a differentiation perspective as they focus on building their >>Business and you think your economics is gonna enable these people to be successful >>100%. That's >>The goal >>100%. >>All right. So another thing that I wanna bring up is the fleet SDK, what was the, that you announced they can, you just quickly share the news on what this >>Is? Yeah, yeah, absolutely. I appreciate that, John. Yeah. So today on the Eve of earth day, we're very excited to announce Mapbox fleet going into, uh, our beta launch and what Mapbox fleet is, is, uh, a set of tools and application that allows our customers to more efficiently route their vehicles, which means lowering their fuel consumption. And at the same time, more efficiently dispatching those vehicles, which means that you can get more done with fewer assets, essentially. How do you get more packages onto a single vehicle to get those to the customers? Now you may be watching the news and understanding, yeah, there's a tremendous explosion of delivery going. Yeah. And that's fantastic. Right? That's great business for our logistics customers. Good business for us too. What's happening though, is that as those volumes are ballooning, everybody's all of a sudden, really looking at their cost structures and trying to understand how do I manage this? >>Right. I have efficiency targets as a business. Maybe I've been really focused on customer acquisition. Now it's time to flip the model and really understand in the economics of profitable growth. We help with that, with that focus on efficiency, but the double edged sword with growth and, and you know, running a logistics business is that you actually have a tremendous amount of carbon emissions that are associated with that. Yeah. For a car to show up or a truck to show up, to deliver something to your house, their emissions associated with that. So what we find is that we're able to not only drive dollar savings for our customers, but also as part of that, with the efficiency angle, really help to drive down the overall carbon impact in the carbon footprint of what they do. And >>How do you do that? >>Well, it's the efficiency lens, right? So if somebody is driving 20%, fewer miles, they're going to emit 20% fewer. Okay. >>Gotcha. So it's a numbers game across the board with actual measurement. That's exactly right. Built in and say optimization paths, all kinds of navigation. >>That's exactly right. So embedded within Mapbox fleet application are those optimization algorithm. >>So you got a platform that's setting up for the next level delivery system slash new way to connect people to goods and services and other things getting from point a to point B, you got the sustainability check you're in the cloud, born in the cloud cloud scale. I gotta a, I can't go without asking if you're on Amazon, you do all this cool stuff. There's gotta be a machine learning an AI angle. So what is that? Yeah, absolutely. >>Absolutely. AB yeah. You know, <laugh> guilty as charged. >>I would say >>John. Uh, so look, I >>Think, I mean AI and, and sustainability are gonna be, I think filings in my, in the future we be talking about on the cube, if you're not an AI company or, and doing force for good stuff, I think there's gonna be mandatory requirements on those. >>I couldn't three more. I think the ESG agenda is an incredibly important one. One that's core to Mapbox has been since the founding of the company back in 2013. Uh, but if you look at how does AI and ML fit into Mapbox, it does that in a number of different ways. And really when we come back to this idea of Mapbox creating a digital twin of the earth, the way that we do that, it is through ingesting a tremendous amount of sensor data. Right? You can imagine, uh, Mapbox customers on any given week drive, billions of miles, we're capturing all of that telemetry data to understand and make sense of what does that mean for, for, for the world that allows us to push in any given day 700,000 updates to our underly, your location technology stack, and at the same time provide insights as to exactly what's happening. Are there roadside incidents? Are there, are there issues with traffic? So by collecting all of that data, we run incredibly powerful AI models on top of it that allow us to create the, the real world representation of what's happening. That's exactly how >>It works. What, what, as they say in the, um, big data AI world is you guys have a tremendous observation space. You're looking at a lot of surface area data that's exactly right. Across multiple workloads and apps. That's >>Exactly >>Right. You can connect those dots with the right AI. >>That's exactly right. That's exactly right. And I think I, you know, coming back to your point around sustainability, I do think that the AI and ML capabilities that are being delivered are going to be paramount to that. It being such a fundamental aspect to what am, to what Mapbox does as a business allows us to launch these game changing solutions like Mapbox fleet and staying on that, that kind of environmental and sustainable kick for a second. Just last week, we launched our, our EV routing API that powers the next generation of EVs. So AI ML sustainability. If it's not core business today, it's gotta very quickly become core. >>It's really interesting. I really think what we're teasing out here and it's fun to talk about it now because we'll look back at it later 10 years or more and say, wow, this is completely refactored the industry and lives and the planet ultimately. Right. So this is a, a kind of got force for good built into the system natively. That's >>Right. That's >>That's interesting, Garrett, thanks so much for sharing the story. Give you the last word, share with the audience, what you guys are up to, what you're promoting, what you're looking for. Are you hiring, uh, is there a call to action? You wanna share? Give the plug for the company? Yeah, >>Absolutely hiring like crazy come join Mapbox and BU build the future of geolocation and intelligent location services with us. Uh, the, thanks so much for the time, >>John. Thanks for coming on cube coverage here in San Francisco, California Mosconi center back at live events. I'm John for host cube stayed with us as day two wraps down. Remember New York city. This summer will be there as well. Cube coverage of more cloud coverage events are back. Thanks for watching.

(calm music) >> Okay, welcome back everyone to theCUBE's live coverage here on the floor at Moscone south in San Francisco California for AWS summit, 2022. This is part of their summit conferences, not re:Invent it's kind of like becoming like regional satellite, mini re:Invents, but it's all part of education developers. Of course theCUBE's here. We're going to be at the AWS summit in New York city, only two this year. And this summer check us out. Of course, re:MARS is another event we're going to be going to so check us out there as well. And of course re:Invent at the end of the year and re:Inforce the security conference in Boston. So, Sarbjeet Johal, our next guest here. CUBE alumni, CUBE influencer, influencer in the cloud industry. Sarbjeet great to see you. Thanks for coming on. Oh, by the way, we'll be at Boston re:Inforce, re:Invent in December, re:MARS which is the robotics AI show, and of course the summit here in San Francisco and New York city, the hot areas. >> That's cool. >> Great to see you. >> Good to see you too. >> Okay. I got a lot of data to report. You've been on the floor talking to people. What are you finding out? What's the report? >> The report is actually, I spoke to three people from AWS earlier. As said one higher up guy from the doctor, Casey Tan. He works on French SaaS chips and he gave me a low down on how that thing works. And there's a systolic arrays TPUs, and like a lot of insider stuff >> Like deep Silicon chip stuff. >> Yes. And that they're doing some great stuff there. And of course that works for us at scale and for cloud guys it's all about scale. If you're saving pennies at that scale, you're saving millions and maybe hundreds of millions at some point. Right? So that was one. And I also spoke to the analytics guys and they gave me some low-down on the Glue announcements. How the big data processing is happening at AWS and how they are now giving you the ability where your infrastructure hugs your demand. So you're not wasting any sources. So that was a number one complaint with the Glue from AWS. So that was one. And then I did the DeepRacing race and my timings were like number 78. So. >> You got some work to do. You download your machine learning module. >> No, I will do that and then play with it. Yes. I will train one. >> You like a simulation too? >> Yeah. Yeah. I will do that simulation, yes. >> What else? Anything jump off the page for you. What's the highlight if you could point at something? Did anything pop up at you in this event with AWS? Was there any aha moment or something that just jumps off the page? >> I think it was mainly sort of incremental to be honest with you. And the one thing-- >> Nothing earth shattering >> Nothing earth shattering and that at the summit it's like that, you know, like it but they are doing new announcements of like almost every day with new services. So I would go home and read on that but there are some patterns that we are seeing emerging and there are some folks very active on Twitter. Mark in recent just did very controversial kind of tweet couple of days back. That was, that was hard. >> Was he shit posting again? >> Shit posting. Yeah. He was shit posting actually, according to actually I saw Corey as well on the floor, Corey and Rodrigo. And, and-- >> Did you see Corey's interview with me? We were talking about shit posting 'cause he wrote in this newsletter. Mark and recently Elon Musk, they're all kind of like they're really kind of active on Twitter with a lot of highly intelligent snarkiness. >> They're super intelligent and they know the patterns, they know the economics and technology. Super smart guys and yeah. Who is in control, there was a move from the middle seat and social media kind of side of things where people are controlling the narratives and who controls the narrative. Is it billionaires? Is it government? We see that. >> Well I mean, it's interesting seeing the power. I mean, I call it the revenge of the nerds. You got the billionaires who are looking at the political screw-ups that Facebook and others have done. And by not being clear and it's hard, it's a hard problem to solve. I don't really want to be in their seat. Even Andy Jassy is the CEO of AWS. What is he? I mean, he's dealing with problems that for some people would be their worst part of like they could ever dream of scenario. He's dealing with that at breakfast. And then throughout his day, he's got all kinds of Amazon's so big and Apple and you got Google and you got the fan companies. So, you know, at some point tech is now so part of society, it's not just the nerds from California. It's tech is in everything now. So it's a societal impact. And so there's consequences for stuff. And so you're starting to see this force for good that's come from the sustainability angle. You're going to start to see force for good with technology as it relates to people's lives. And we had Mapbox on the CUBE and they provide all this navigation and Gareth the guy who runs that division, he talks about dark kitchens, dark stores. So just they're re-engineering the supply chain of delivery. So we all been to restaurants and seen people there from picking up food delivery. Why are they going to the retail? So dark kitchens are just basically depots for supplying the 10 menus that everyone orders from. That's a change of a structural change in the industry. So that's jumped out at me, Matt Wood spoke to me about serverless impact to the analytics team. And again, structural changes, technical and culture. Right? So, so you're starting to see to me more and more of the two themes of some technology change, architectural change, system change and culture thinking. And you know, we had a 20 year old guest on here who was first worked at Amazon web services when he was 16. >> Wow. >> Graduated high school early and went into Amazon. He's like, I love tools. So people love tools. Hardware is coming back. Right? So I mean Sarbjeet this is crazy. >> It's crazy. >> What's going on. >> It's crazy actually. Remember the nine year old kid at re:Invent 2019. Karthick was the name if I remember, but I spoke to him and he was crazy. He was AWS certified and kids are playing with this technology in their high schools. >> It's awesome. >> And even in their elementary schools now. >> They can get their hands on it quicker. They don't need to go in full class for a year. They can self-teach, they can do side projects they can launch a side hustle, they can stand up a headless retail outlet, who knows what they can do if you got the Lego blocks. This is what I love about the cloud, you can really show something fast and then abandon it. >> Actually, I think it is all enabled through cloud. Like the accessibility of technology has gone like exponentially, like wildfire. Like once you have access to the cloud just all you need is connection to the internet. After that you have the VMs. and you have the serverless, there's zero cost to you. And things are thrown at you. Somebody who was saying that earlier here like we have said that many times it's like that's how the drug dealer, you know, sell the drug. Like sniff it, it's free, >> First is free. >> So they're doing it. Yes. >> We say that about theCUBE. >> And from the, I see cloud from two different angles, like we all do. And like, I try to sort of force myself to look at it from the both angles. There's the supplier side and the buyer side or the consumer side on the other side. Right? So from the supplier side, it's a race for talent to build it, number one, then number two is race for talent to train them. So we saw the numbers and millions being shown today at the keynote again. And Google is showing those numbers as well. Like how many millions they are training like 25 to 30 million people within next two, three years. It's crazy numbers. >> Sarbjeet I got to say so if I have to look at what jumped off the page for me on this event, was couple things and this is kind of weird nuanced stuff but I'll just try to explain it as best I can. Number one, we're going to see more managed services like DevOps managed services. As DevOps teams grow, talent is a problem. And Kubernetes obviously is growing and got to get that right. It's not easy to be a Kubernetes, you know slinging clusters around with Kubernetes. It's hard. I think that's got to get easier. So I think the path to easy is going to be some sort of abstraction service layer. And I think the smart people are going to have this layer will manage it and then provide that as a service, number one. Number two is this notion of a systems design thinking around elements, whether it's storage or maps for like Mapbox and around these elements they have to have a systematic effect of other things. You can't just, if it changes, it's going to have consequences that's what systems do. So, tooling being built around these elements and they have to have hardened APIs that is clear. People who are trying to be "cloud native" need to get this right. And you have to have the tooling in and around the the element and then have APIs to connect and then glue up. So it's interesting. Clearly those things are happening and multiple conversations, people were teasing that out. And then obviously the super cloud was coming in. >> Is there. >> Mapbox is basically a super cloud. They're like what snowflake is for data analytics. They are for-- >> MongoDB is another one. >> MongoDB's got Atlas. I mean, MongoDB was criticized for years. Doesn't scale. Remember the old lamp stack days, they were preferred. They're document, they nailed it with document. The document aspects of data, but they were always getting criticized. They can't scale. And they just keep scaling. But now with Atlas, they're on AWS. It's just, auto scale. So that's killer for MongoDB. So I think their stock price is undervalued my opinion but you know, I don't give legal advice. >> I think that the whole notion of-- >> Or financial advice. >> The multicloud, right? So for a multicloud to kill that complexity of multicloud, we have to go to the what Dave Vellante and you guys say super cloud, right? Another level of abstraction on top of infrastructure provider by AWS, Google cloud, Azure. So that's where we're going. >> Well, Dave and I debate this right, he bundles multi-cloud in there and most people think that's what he's saying but I'm saying multi-cloud is a reality. I mean, multi-cloud means you're going to have multiple clouds. They're just not you're not sharing workloads across those clouds. It's like not the same workload. That's not going to yet happen. I run Azure because I have 365, that's it. I run Amazon for everything else. That's kind of the use case. But to me, super cloud is building on top of AWS or Azure where you leverage their CapEx and create differentiated value. It's your own cloud without all the CapEx but it's got to be like super integrated and the benefit's got to be so good that it seems like pennies to your point earlier. >> Yeah. >> And the economics to the applications in it are just so obvious and they got to be they got to be so big for the application developer. So that's to me is super cloud. And then of course having the connected tissue to manage the transit around multiple clouds. >> Yeah. I think they have it too. I totally agree with you. But another thing is from having the developer background I think the backward compatibility is a huge issue in cloud. >> Yeah. I agree. >> It's a lot of technical debt being built and I hear that, I'm hearing that more and more. I think that we have to solve as industry as like these three main players have to solve that problem. So that's one big thing, actually. I'm very like after, you know, like to talk about it and all that stuff. So yeah. It's another thing is another pattern actually to all the cloud naysayers out there, right? Is that those are the people who come from the hardware background. So I've seen another pattern out there. So I'm trying to synthesize, who are these people who bash cloud all the time? I'm pro-cloud of course everybody knows that. >> We know you're pro, we're all pro cloud. We're totally biased. We love cloud >> Actually. No, I've seen both sides. I've seen both sides. I've worked at EMC, VMware, I worked at Oracle cloud as well. And then, and before that I have written a lot of software. A software developer is pro-cloud. A typical hardware ops guy or girl, they are pro on-prem or pro hybrid and all that. Like they try to keep it there. >> I think first of all, I have opinion on this. I think, I think you're right. But how hardware is coming back, if you look at how cloud is enabling hardware, it's retro, it's designed for the cloud. So hardware's going to offload, either accelerate stuff and offload stuff from the software guide. So look at DeepRacer it's hardware. Now it's a car. You've got the silicon and the chips. So the chips you're talking about. Those aren't chips for service and the data center. They're just chips to make the software in the cloud run better. >> Sarbjeet: Well scale. >> So scaling. And so I think we're going to see a Renaissance in hardware. It's going to look different. It's going to act different. So we're watching this. I mean, you brought up the idea of having a CUBE hardware box. >> Yeah. It's a great idea. >> It's a good idea. DM me and tell me it's a bad idea or good idea. I'll blame Sarbjeet for that. But what else have you learned? >> What else have learnt actually it's basically boils down to economics at the end of the day. It's about moving fast. It's about having developer productivity, again going back the cloud naysayers. It's like, why did you build a bike? Remember Steve Job used to say that, "computer is the bicycle for the human minds." >> Yes. >> Right. So cloud is the bicycle for the enterprises. They makes them move faster. 'So I think that's-- >> All right. We're closing down. We're going to hold on until they pull the plug on theCUBE literally. Sarbjeet great to see you on there. Check 'em out on Twitter. Great event. Good to see you, great report. Thank for sharing. Sarbjeet Johal here on theCUBE, taking over our community site I hear, right? Now you going to work-- >> I'm there. I'm always there. >> Great to have you on. I'm going to work on some new things with theCUBE. Really appreciate working with us. Thanks a lot. >> I really appreciate you guys giving me this platform. It's an amazing platform. Thank you very much. >> That's all right. We'll be back. That's it for our coverage of AWS summit 2020 here live on the floor. Events are back. Hybrid's back. We get theCUBE studios in Palo Alto in Boston. Re:invent at the end of the year but we're going to the summit in New York city. In the summer, we got re:Inforce in Boston the security conference. Re:MARS which is the robotics IML conference. And of course the big summit New York and San Francisco we're there of course. Share thecube.net for all the action. I'm John for your host with Sarbjeet here. Closing out the show. Thanks for watching. (Calm music)

[Narrator] - You are a CUBE Alumni. (cheerful music) Live, from Silicon Valley, it's theCUBE. Covering Google cloud Next '17 (rhythmic electronic music) >> Welcome back everyone. We're live here in the Palo Alto Studio for theCUBE, our new 4500 square foot studio we just moved into a month and a half ago. I'm John Furrier here, breaking down two days of live coverage in-studio of Google Next 2017, we have reporters and analysts in San Francisco on the ground, getting all the details, we had some call-ins. We're also going to call in at the end of the day to find out what the reaction is to the news, the key-notes, and all the great stuff on Day one and certainly Day two, tomorrow, here in the studio as well as in San Francisco. My next guest is Tyler Bell, good friend, industry guru, IOT expert, he's been doing a lot of work with IOT but also has a big data background, he's been on theCUBE before. Tyler, great to see you and thanks for coming in today. >> Thanks, great to be here. >> So, data has been in your wheelhouse for long time. You're a product guy, and The cloud is the future hope, it's happening big-time. Data, the Edge, with IOT is certainly part of this network transformation trend. And, certainly now, machine-learning and AI is now the big buzzword. AI, kind of a mental-model. Machine-learning, using the data. You've been at the front-end of this for years, with data and Factual and Mapbox, your other companies you worked for. Now you have data sets. So before it was like a ton of data, and now it's data sets. And then you got the IOT Edge, a car, smart city, a device. What's you take on the data intersecting with the cloud? What are the key paradigms that are colliding together? >> Yeah, I mean the reason IOT is so hot right now is really 'cause it's connecting a number of things that are also hot. So, together, you get this sort of conflagration of fires, technology fires. So, on one side you've got massive data sets. Just huge data sets about people, places and things that allow systems to learn. So, on the other end, you've got, basically, large-scale computation, which isn't only just available, it's actually accessible and it's affordable. Then, on the other end, you've got massive data collection mechanisms. So, this is anything from the mobile phone that you'll hold in your pocket, to a LIDAR, a laser-based sensor on a car. So, this combination of massive, hardware derived data collection mechanisms, combined with a place to process it, on the cloud, do so affordably. In addition to all the data, means that you get this wonderful combination of the advent of AI and machine-learning, and basically the development of smart systems. And that's really what everybody's excited about. >> It's kind of intoxicating to think about, from a computer science standpoint, this is the nirvana we've been thinking about for generations. With the compute now available, we have, it's just kind of coming together. What are the key things that are merging in your mind? 'Cause you've been doing a lot of this big data stuff. When I say big, I mean large amounts, large-scale data. But as it comes in, as they say, the world's, the future's here, but it's evenly distributed. You could also say that same argument for data. Data's everywhere, but it's not evenly distributed. So, what are some of the key things that you see happening that are important for people to understand with data, in terms of using it, applying it, commercializing it, leveraging it? >> Yeah, what you see, or what you have seen previously is the idea of data, in many people's minds, has been a data base or it's been sort of a CSV file of rows and columns and it's been this sort of fixed entity. And what you're seeing now is that, and that's sort of known as structure data, and what you're seeing now is the advent of data analytics that allow people to understand and analyze loose collections of data and begin to sort of categorize and classify content. In ways that people haven't been able to do so previously. And so, whereas you used to have just a data base of sort of all the places on the globe or a whole bunch of people, right now you can have information about, say, the images that camera sensors on your car sees. And because the systems have been trained about how to identify objects or street signs or certain behaviors and actions, it means that your systems are getting smarter. And so what's happening here is that data itself is driving this trend, where hardware and sensors, even though they're getting cheaper and they're getting increasingly commoditized, they're getting more intelligent. And that intelligence is really driven by, fundamentally, it's driven by data. >> I was having a conversation, yesterday, at Stanford there was a conference going on around bias and data. Algorithms now have bias, gender bias, male bias, but it brings up this notion of programmability and one of the things that some of the early thinkers around data, including yourself, and also we extend that out to IOT, is how do you make data available for software programs, for the learning piece? Because that means that data's now an input into the software development process, whether that's algorithms on the fly being developed in the future or data being part of the software development kit, if you will. Is that a fantasy or is that gettable, is that in reach? Is it happening? Making data part of that agile process, not just a call to a data base? >> Exactly, a lot of the things, the most valuable assets now are called basically labeled data sets, where you could say that this event or this photo or this sound even has been classified as such. And so it's the bark of a dog or the ring of a gunshot. And those labeled data sets are hugely valuable in actually training systems to learn. The other thing is, if you look at it from, say, AV, which has a lot in common with IOT, but the data set is less about a specific sort of structured or labeled event or entity. And instead, it's doing something like putting, there's one company where you can put your camera on the dashboard of your car and then you drive around and all this does is just records the images and records which way your car goes, and, that's actually collecting and learning data. And so, that kind of information is being used to teach cars how to drive and how to react in different circumstances. And so, on one hand, you've got this highly-structured labeled data, on the other hand, it's almost machine behavioral data, where to teach a car how to drive, cars need to understand what that actually entails. >> Yeah, one of the things we talked about on Google Next earlier in the day, when we saw a couple earlier segments. I was talking about, I didn't mean this as a criticism to the enterprise, but I was just saying, Google might want to throttle back their messaging or their concepts. Because the enterprise kind of works at a different pace. Google is just this high-energy, I won't say academic, but they're working on cutting-edge stuff. They have things like Maps, and they're doing things that are just really off the charts, technically. It's just great technical prowess. So, there's a disconnect between enterprise stuff and what I call 'pure' Google cloud. The question that's now on the table is, now with the advent of the IOT, industrial IOT, in particular, enterprises now have to be smarter about analog data, meaning, like the real world. How do you get the data into the cloud from a real-world perspective? Do you have any insight on that? it's something that hard to kind of get, but you mentioned that cam on the car, you're essentially recording the world, so that's the sky, that's not digitized. You're digitizing an analog signal. >> Yeah, that's right. I think I'd have two notes there. The first is that, everything that's going on that's exciting, is really at this nexus between the real world, that you and I operate in now and how that's captured and digitized, and actually collected online so it can be analyzed and processed and then affected back in the real world. And so, when you hear about IOT and cars, of course there are sensors, which basically do a read type analysis of the real world, but you also have affecters which change it and servos, which turn your tires or affect the acceleration or the braking of a vehicle. And so, all these interesting things that are happening now, and it really kicked off, of course, with the mobile phone, is how the online, data-centric, electric world connect with the real world. And all of that's really, all that information is being collected is through an explosion of sensors. Because you just have, the mobile phone supply chains are making cameras, and barometers, and magnetometers, all of these things are now so increasingly inexpensive that when people talk about sensors, they don't talk about one thousand dollar sensor that's designed to do one thing, instead there's thousands of $1 sensors. >> So, you've been doing a lot of work with IOT, almost the past year, you've been out in the IOT world. Thoughts on how the cloud should be enabled or set up for ingesting data or to be architected properly for IOT-related activities, whether it's Edge data store, or Edge Data, I mean, we have little things as boring as backup and recovery are impacted by the cloud. I can imagine that the IOT world, as it collides in with IT, is going to have some reinvention and reconstruction. Thoughts on what the cloud needs to do to be truly IOT ready? >> Yeah, there's some very interesting things that are happening here and some of them seem to be in conflict with each other. So, the cloud is a critical part of the IOT entire stack and it really goes from the device of a sensor, all the way to the cloud. And what you're getting is you are getting providers, including Google and Amazon and SAP and there's over 370, last count, IOT platform providers. Which are basically taken their particular skill set and adjusted it and tweaked it and they now say that we now have an IOT platform. And in traditional cloud services, the distinguishing features are things like being able to have record digital state of sensors and devices, sort of 'shadow' states, increased focus on streaming technology over MAP-reduced batch technology, which you got in the last 10 years, through the big data movement, and the conversations that you and I have had previously. So, there is that focus on streaming, there is a IOT-specific feature stack. But what's happening is that because so much data is being corrected. Let's imagine that you and I are doing something where we're monitoring the environment, using cameras, and we have 10,000 cameras out there. And, this could be within a vehicle, it could be in a building, or smart city, or in a smart building. Cameras are, the cloud traditionally accepts data from all these different resources, be it mobile phones, or terminals and collects it, analyzes it, and spits it back out in some kind of consumable format. But what's happening now is that IOT and the availability of these sensors is generating so much data that it's inefficient and very expensive to send it all back to the cloud. And so all of these-- >> And, it's physics, too. There's a lot of physics, right? >> Exactly, and all these cameras sending full raster images and videos back to the cloud for analysis. Basically the whole idea of real time goes away if you have that much data, you can't analyze it. So, instead of just the cameras sending out a single dumb raster image back, you teach the camera to recognize something, So you could say "I recognize a vehicle in this picture" or "I recognize a stop sign" or a street light. And instead of sending that image back to be analyzed on the cloud, the analysis is done on the device and then that entity is sent back. And so, the sensor says "I saw this stop sign "at this point, at this time in my process." >> So this cuts back to the earlier point you were making about the learning piece, and the libraries, and these data sets. Is that kind of where that thread connects? >> Exactly, so to build the intelligence on the device, that intelligence happens on the cloud. And so, you need to have the training sets and you need to have massive GPUs and huge computational power to instruct. >> Thanks Intel and NVIDIA, we need more of those, right? >> Indeed, and so, that's what's happening on the cloud, and then those learnings are basically consolidated and then put up on the device. And, the device doesn't need the GPUs, but the device does need to be smart. And so, in IOT, especially look for companies that understand, especially hardware companies, that understand that the product, as such, is no longer just a device, it's no longer just a sensor, it's an integral combination of device, intelligence platform in the cloud, and data. >> So, talk about the notion of, let's talk about the reconstruction of some of the value creation or value opportunities with what you just talked about 'cause if you believe what you just said, which I do believe is right on the money, that this new functionality, vis-a-vis, the cloud, and the smart ads and learning ads, and software, is going to change the nature of the apps. So, if I'm a cloud provider, like Google or Amazon, I have to then have the power in the cloud, but it's really the app game, it's the software game that we're talking about here. It's the apps themselves. So, yeah, you might have an atom processor has two cores versus 72 cores, and xeon, and the cloud. Okay, that's a device thing, but the software itself, at the app level, changes. Is that kind of what's happening? Where's the real disruption? I guess what I'm trying to get at is that, is it still about the apps? >> Yeah, so, I tend not to think about apps much anymore, and I guess, if you talk to some VCs, they won't think about apps much anymore either. It's rather, it tends to, you and I still think, and I think so many of us in Silicone Valley, still think of mobile phones as being the end point for both data collection and data effusion. But, really one of the exciting things about IOT now, is that it's moving away from the phone. So, it's vehicles, it's the sensors in the vehicles, it's factories, and the sensors in the factories, and smart cities. And so, what that means is you're collecting so much more data, but also, you're also being more intelligent about how you collect it. And so, it's less about the app and it's much more about the actual intelligence, that's baked into the silicon layer, or the firmware of the device. >> Yeah, I tried to get you on their Mobile World Congress special last week and we're just booked out. But I know you go to Mobile World Congress, you've been there a lot. 5G was certainly a big story there. They had the new devices, the new LG phones, all the sexy glam. But, the 5G and the network transformation becomes more than the device, so you're getting at the point which is it's not about the device anymore, it's beyond the device, more about the interplay between the back at the network. >> It is, it's the full stack, but also it's not just from one device, like the phone is one human, one device, and then that pipeline goes into the cloud, usually. The exciting thing about IOT and the general direction that things are moving now, it's what can thousands of sensors tell us? What can millions of mobile phones, driven over a 100 million miles of road surface, what can that tell us about traffic patterns or our cities? So, the general trend that you're seeing here is that it's less about two eyeballs and one phone and much more about thousands and millions of sensors. And then how you can develop data-centric products built on that conflagration of all of that data coming in. And how quickly you can build them. >> We're here with Tyler Bell, IOT Expert, but also data expert, good friend. We both have kids who play Lacrosse together, who are growing up in front of our eyes, but let's talk about them for a second, Tyler. Because they're going to grow up in a world where it's going to be completely different, so kind of knowing what we know, and as we tease-out the future and connect the dots, what are you excited about this next generation's shift that happening? If you could tease-out some of the highlights in your mind for, as our kids grow up, right, you got to start thinking about the societal impact from algorithms that might have gender bias, or smart cities that need to start thinking about services for residents that will require certain laning for autonomous vehicles, or will cargo (mumbles). Certainly, car buying might shift. They're cloud-native, they're digital-native. What are you excited about, about this future? >> Yeah, I think it's, the thing that's, I think, so huge that I have difficulty looking away from it, is just the impact, the societal impact that autonomous vehicles are going to have. And so, really, not only as our children grow up, but certainly their children, our grandchildren, will wonder how in the heck we were allowed to drive massive metal machines, and just anywhere-- >> John: With no software. >> Yeah, with really just our eyeballs and our hands, and no guidance and no safety. Safety's going to be such a critical part of this. But, it's not just the vehicle, although that's what's getting everybody's attention right now, it's really, what's going to happen to parking lots in the cities? How are parking lots and curb sides going to be reclaimed by cities? How will accessibility and safety within cities be affected by the ability to, at least in principle, just call an autonomous vehicle at any time, have it arrive at your doorstep, and take you where you need to go? What does that look like? It's going to change how cars are bought and sold, how they're leased. It's going to change the impact of brands, the significance of, are these things going to be commoditized? But, ultimately, I think, in terms of societal impact, we have, for generations, grown up in an automotive world, and our grandchildren will grow up in an automotive world, but it will be so changed 'cause it will impact entirely what our cities and our urban spaces look like. >> The good news is when they take our drivers licenses away when we're 90, we'll, at least be able to still get into a car. >> There's places we can go. >> We can still drive (laughs) >> Exactly, exactly, the time is right. We may not have immortality, but we will be able to get from one place to another in our senility. >> We might be a demographic to buy a self-driving car. Hey, you're over 90, you should buy a self-driving car. >> Well, it'll be more like a consortium. Like you, I, and maybe 30 other people. We have access to a car or fleet. >> A whole new man cave definition to bring to the auto,. Tyler, thanks for sharing the insight, really appreciated the color commentary on the cloud, the impact of data, appreciate it. We're here for the two days of coverage of Google Next here inside theCUBE. I'm John Furrier, thanks for watching. More coverage coming up after this short break. (cheerful music) (rhythmic electronic music) >> I'm George--

>>From New York expecting the signal to nine. It's the cube covering sparks summit east brought to you by spark summit. Now your hosts, Dave Volante and George Gilbert >>Right here in Midtown at the Hilton hotel. This has sparked somebody and this is the cube. The cube goes out to the events. We extract the signal from the noise. Jack Norris is here. He's the CMO of Mapbox, long time cube, alum jackets. It's great to see you again. Hey, if you've been here since the beginning of this whole big data >>Meme and it might've started here, I don't know. I think we've yeah, >>I think you're right. I mean, it really did start it. I think in this building, it was our first big data show at the original, you know, uh, uh, Hadoop world. And, uh, and you guys, like I say, I've been there from the start. Uh, you were kind of impatient early on. You said, you know, we're just going to go build solutions and, uh, and ignore the noise and you built a really nice, nice business. Um, you guys have been growing, you're growing your Salesforce and, uh, and things are good and all of a sudden, boom, the spark thing comes in. So we're seeing the evolution. I remember saying to George and the early days of a dupe, we were geeking out talking to all the bits and bytes and then it turned into a business discussion. It's like we're back to the hardcore bits and bites. So give us the update from Matt bar's point of view, where are we in the whole big data space? >>Well, I think, um, I think it has transitioned. I mean, uh, if you look at the typical large fortune company, the web to Datto's, it's really, how do we best leverage our data and how do we leverage our data in that we can, we can make decisions much faster, right? That high-frequency decision-making process. Um, and typically that involves taking production data and analytics and joining them together so that you're actually impacting business as it happens and to do that effectively requires, um, innovations. So the exciting thing about spark is taking and, uh, and having a distributed compute engine, it's much easier to develop and, uh, in much faster. >>So in the remember the early days we'd be at these shows and the big question was, you know, can you take the humans out of the equation? It's like, no, no humans are the last mile. Um, is that, is that changing or would we still need that human interaction or, >>Um, humans are important part of the process, but increasingly if you can adjust and make, you know, small algorithmic decisions, um, and, and make those decisions at that kind of moment of truth, you got big impact, and I'll give you a few examples. So, um, ad platforms, you know, Rubicon project over a hundred billion ad auctions a day, you know, humans, part of that process in terms of setting that up and reviewing the process, but each, you know, each supply and demand decision, there is an automated decision optimizing that has a huge impact on the bottom line, um, fraud, uh, you know, credit card swiping that transaction and deciding is this fraudulent or not avoiding false positives, et cetera, a big leveraged item. So we're seeing things like that across manufacturing, across retail healthcare. And, um, it isn't about asking bigger questions or doing reports and looking back at, you know, what happened last week. It's more, how can I have an infrastructure in place that allows this organization to be agile? Because it's not the companies with the most data that's going to win. It's the companies that are the most agile and making intelligent. >>So it's so much data. Humans can ingest it any faster. I mean, we just, we can't keep up. So the world needs data scientists that needs trained developers. You've got some news I want to talk about on the training side, but even that we can only throw so many bodies at the problem. So it's really software. That's going to allow us to scale it. Software's hard. Software takes time. So we've seen a lot of the spend in the analytics, big data world on, on services. And obviously you guys and others have been working hard to shift it towards software. I want to come back to that training issue. We heard this morning about, uh, Databricks launched a move. They trained 20,000 people. That's a lot, but still long way to go. You guys are putting some investment into training. Talk about that news. Yeah. >>Yeah. Um, well it starts at the underlying software. If you can do things in the platform to make it much easier and do things that are hard to surround with services, like, uh, data protection, right? If you've lost data, it doesn't matter how many people you throw at it, you can't recover it. Right. So that's kind of the starting point you're gonna get fired. >>The, the, uh, the approach we've taken is, is to take, uh, a software product approach to the training as well. So we rolled out on demand training. So it's free, it's on demand. You work at your own pace. It's got different modules, there's some training associated with that, or some hands-on labs, if you will. Um, we launched that last January. So it's basically coming up the year anniversary. We recently celebrated, we trained 50,000 people, uh, on, on Hadoop and big data. Um, today we're announcing expansion on spark classes. We've got full curriculum around spark, including a certification. So you can get sparked certification through this, this map, our on demand training. Okay. >>Gotcha. You said something really, really intriguing that I want to dive into a little bit is where we were talking about the small decisions that can be made really, really fast for that a human in the loop human might have to train them, but it at runtime now where you said, it's not about asking bigger questions, it's finding faster answers, um, what had to change in your platform or in the underlying technology to make that possible. >>You know, um, there's a lot that into it. It's typically a series of functions, uh, a kind of breadth that needs to be brought to the problem as well as squeezing out latencies. So instead of, um, the traditional approach, which is different applications and different analytic techniques dictate a separate silo, a separate, you know, scheme of data. And you've got those all around the organization and data kind of travels, and you get an answer at the end of some period of time. Uh, it's converging that altogether into a single platform, squeezing out those latencies so that you can have an informed action at the speed of business, if you will. And, >>Um, let's say spark never came along. Would that be possible? >>Yes. Yes. Would you, how would you, so if you look at kind of the different architectures that are out there, there's typically deep analytics in terms of, you know, let's go look at the trends, you know, the last seven years, what happened. And then look, let's look at, um, doing actions on a streaming set, say for instance, storm, and then let's do a real time database operations. So you could do that with, with HBase or map RDB and all of that together. What spark has really done is made that whole development process just much easier and much more streamlined. And that's where a lot of the excitements happen. >>So you mentioned earlier, um, to, to use cases, ad tech and fraud detection. Um, and I want to ask you about those in the state of those. So ad tech obviously has come a long way, but it's still got a ways to go. I mean, you look at, I mean, who's making money on ads. Obviously Google will make tons of money. Everybody else is sorta chasing them Facebook making money. It's probably cause they didn't let Google in. Okay. So how will spark affect sort of that business? Uh, and, and what's map, R's sort of role in evolving that, you know, to the next level. >>So, so, um, there's, there's different kind of compute and the types of things you can do, um, on the data. I think increasingly we're seeing the kind of streaming analytics and making those decisions as the data arrives, right. And then there's the whole ecosystem in terms of how do you coordinate those flows of data? It's not just a simple, here's the origin, here's the destination. There's typically a complex data flow. Um, that's where we've kind of focused on map our streams, this huge publish and subscribe infrastructure so that you can get real-time data to the appropriate location and then do the right operations, a lot of that involved with spark, but not exclusively. >>Okay. And then on fraud detection, um, obviously come a long way. Sampling could have died. Yes. And now, but now we're getting too many false positives. You get the call and, you know, I mean, I get a lot of calls because we can buy so much equipment, but, um, but now what about the next level? What are you guys doing to take fraud detection to the next level? So that when I get on the plane in Boston and I land in London, it knows, um, is that a database problem? Is it an integration problem, a systems problem, and how, what role you guys play in solving that? >>Well, there's, there's, um, you know, there's, there's a lot of details and techniques that probably go, um, beyond, you know, what, what we'll share publicly or what are our customers talk about publicly? I think in general, it's the more data that you can apply to a problem. The more context, the better off you are, that's the way I kind of summarize it so that instead of a sampling or instead of a boy, that's a strange purchase over there, it's understanding, well, this is Dave Valenti and this is the full body of, of, uh, expenditures he's done, then the types of things and here's who he frequently purchases from. And here's kind of a transaction trend started in San Francisco, went to New York, et cetera. So in context it would make more sense. So >>Part of that is more data. And the other part of that is just better algorithms and better, better learnings and applying that on a continuous basis. How are your customers dealing with that, that constraint? I mean, if they got a, a hundred dollars to spend, yeah. They can only spend so much on, on each of those gathering more data, cleaning the data, they spent so much time getting it ready versus making their machine learning algorithms or whatever the other techniques to do. What are you seeing there as sort of best practice? It was probably varies. I'm sure, but give us some color on it. >>Um, I'll actually go back to Google and Google a letter last round, um, you know, excellent, excellent insights coming from Google. They wrote a paper called the unreasonable effectiveness of data and in it, they basically squarely addressed that problem. And given the choice to invest in either the complex model and algorithm or put more data at it, putting more data, had a huge impact. And, um, you know, my simple explanation is if you're sampling the data, you have to have a model that tries to recreate reality. If you're looking at all of the data, then the anomalies can, can pop up and be more apparent. And, um, the more context you can bring, the more data from other sources. So you get around, you know, a better picture of what's happening, the better off you are. And so that requires scale. It requires speed and requires different techniques that can be brought to bear, right? The database operation, here's a streaming operation, here's a deep, you know, file machine learning algorithm. >>So there's a lot of vendors in the sort of big data ecosystem are coming at spark from different angles and, um, are, are trying to add value to it and sort of bathe themselves in sort of the halo. Yep. Now you guys took some time upfront to build a converged platform so that you weren't trying to wrap your arms around 37 different projects. Can you tell us how having perhaps not anticipated spark how this converts platform allows you to add more value to it than other approaches? >>So, so we simplify, if you look at the Hadoop ecosystem, it's basically separated into the components for compute and management on top of the data layer, right? The Hadoop distributed file system. So how do you scale data? How do you protect it? It's very simply what's going on. Spark really does a great job at that top layer. Doesn't do anything about defining the underlying storage layer in the Hadoop community that underlying storage layer is a batch system. So you're trying to do, you know, micro batch kind of streaming operations on top of batch oriented data. What we addressed was to take that whole data layer, make it real time, make it random. Read-write converge enterprise storage together with Hadoop support and spark support on a single platform. And that's basically >>With the difference and to make an enterprise great. You guys were really the first to lead the lecture. You were, everybody started talking about attic price straight after you were kind of delivering it. So you've had a lead there. Do you feel like you still have a lead there, or is that the kind of thing where you sort of hit the top of the S-curve and start innovating elsewhere? >>NC state did a study, uh, just this past year, a recent study identified that only 25% of data corruption issues are identified and properly handled by the Hadoop distributed file system. 42% of those are silent. So there's a huge gap in terms of quote unquote enterprise grade features and what we think. >>Yes, silent data corruption has been a problem for decades now. And you're saying it's no different in the duke ecosystem, especially as, as mainstream businesses start to, uh, to adopt this what's happening in the valley. Uh, we're seeing, you know, in the wall street journal every day you read about down rounds, flat rounds, people can't get B rounds. Uh, you guys are funded, you know, you're growing, you're talking about investments, you know, what do you see? Do you, do you feel like you're achieving escape velocity? Um, maybe give us sort of an update on, uh, the state of the business. >>Yeah. I, I think the state of the business is best represented by the customers, right? And the customers kind of vote, right. They vote in terms of, you know, how well is this technology driving their business? So we've got a recent study, um, that kind of shows the, the returns that customers, um, are getting, uh, we've got a 1% chance, a 99% retention rate with our customers. We've got, uh, an expansion rate. That's, that's unbelievable. We've got multi-million dollar customers in, uh, in seven of the top verticals and nine out of the top $10 million customers. So we're seeing significant investments and more importantly, significant returns on the part of customers where they're not just doing a single application on the platform, but multiple >>Applications, Jack Norris map are always focused. Always a pleasure having you on the cube. Thanks very much for coming on. Appreciate it. Keep right there, buddy. We'll be back with our next guest is the cube we're live from spark somebody's right back. Okay.

>>Live from New York city. It's cute. here's your host? Jeff Frick. >>Hi, Jeff Frick here with the Q we're on the ground at the USS Intrepid at the Hadoop on the Hudson party put on by Matt BARR. It's uh, I think it's the party of the night tonight here in big data week, New York city with strata cough, a dupe world, big data NYC. So Jack a great >>Venue. Yeah, it's excellent. Here. >>The place is filled. I'm just struck by the technology. There's a Gemini capsule over there, about 50 years old. It's about the size of a Volkswagen, I think would be much bigger. And to think that those guys went up into space with probably less technology than is on your four year old flip phone. Amazing. Yeah. >>Not, not much data at all. No. If >>You look at it, just kind of get that bounce on the gravity thing, which I never quite understood. So talk about you guys had some big news today. Once you give us a rundown on some of the announcements, >>We had two big announcements. One was incorporating the map RDB and our community edition that came out. We also reported results from our customers where the majority of customers reported less than a 12 month payback, uh, 65% of five X or greater return and 40%, 10 X or greater. And that included a subset of those customers that had experienced with other distributions. So kind of a Testament to when you get serious about Hadoop, you get serious with Mapbox >>And when they're getting those return on investments, we're always trying to explore where's the big, the big ROI, because it's really in value that's released for the customer. It's not necessarily because it's a cheaper way to do it, >>Right? So, so there are some costs that 63% was cost reduction that was driving it about 41% were top-line revenue projects. And about 23% were related to risk reduction and risk mitigation. And if you add those up, it's greater than a hundred percent because of many customers that are doing multiple applications. >>Great. So you've been coming to Hadoop world for longer than you would admit to me before we came on camera and, and the baseball playoffs are going on right now. I mean, we like to talk in sports analogy. So kind of where are we in, in kind of what inning are we in this adoption of big data and the duke specifically >>Early, early innings. Um, but, uh, what we've seen is the bases are loaded and we're up >>And it's it. And it seems to be we're way past now the POC stage. Now we're really getting in there for that. >>And the, the customer announcement, we did kind of shows how people are hitting it out of the park with Hadoop. And a lot of that is by impacting the operations, impacting the business as it happens. And that's coupling analytics plus this higher arrival rate data from a variety of sources and making adjustments so that you can impact revenue as businesses happening. You can mitigate risk as it's happening. It's not just reporting, looking back >>Function. Right, right. It's being able to react in real time, which is defined by, in time to do something about it. Right. Exactly. All right. Well, thanks for hosting a great party, Jack Norris. Here we are on the ground, uh, at the USS Intrepid at the Hadoop on the Hudson. Uh, uh, if you take a nice picture, tweet that in. I think they got some prizes. Hadoop Hudson is a hashtag Jeff Frick on the ground. You're watching the cube. Thanks. Big ship.

>>Live from Las Vegas, Nevada. It's a queue at HP. Discover 2014 brought to you by HP. >>Welcome back, everyone live here in Las Vegas for HP. Discover 2014. This is the cube we're out. We go where the action is. We're on the ground here at HP. Discover getting all the signals, sharing them with you, extracting the signal from the noise. I'm John furrier, founder of SiliconANGLE. I joined Steve Woolwich VP of product marketing at map art technologies. Great to see you welcome to the cube. Thank you. I know you got a plane to catch up, but I really wanted to squeeze you in because you guys are a leader in the big data space. You guys are in the top three, the three big whales map are Hortonworks, Cloudera. Um, you know, part of the original big data industry, which, you know, when we did the cube, when we first started the industry, you had like 30, 34 employees, total combined with three, one company Cloudera, and then Matt are announced and then Hortonworks, you guys have been part of that. Holy Trinity of, of early pioneers. Give us the update you guys are doing very, very well. Uh, we talked to you guys at the dupe summit last week. So Jack Norris for the party, give us the update what's going on with the momentum and the traction. And then I want to talk about some of the things with the product. >>Yeah. So we've seen a tremendous uptick in sales at map. Are we tripled revenue? We announced that publicly about a month ago. So we went up 300% in sales, over Q3, I'm sorry, Q1 of 2013. And I think it's really, you know, the maturity of the market. As people move more towards production, they appreciate the enterprise features. We built into the map, our distribution for Hadoop. So, um, you know, the stats I would share is that 80% of our customers triple the size of their cluster within the first 12 months and 50% of them doubled the size of the cluster because there's the, you know, they had that first production success use case and they find other applications and start rolling out more and more. So it's been great for us. >>You know, I always joke with Jack Norris, who's the VP of marketing over there. And John Frodo is the CEO about Matt bars, humbleness. You don't have the fanfare of all the height, depressed love cloud era. Now see they had done some pretty amazing things. They've had a liquidity event, so essentially kind of an IPO, if you will, that huge ex uh, financing from Intel and they're doing great big Salesforce. Hortonworks has got their open source play. You guys got, you got your heads down as well. So talk about that. How many employees you guys have and what's going on with the product? How many, how many new, what, how many products do you guys actually, >>We have, well, we have one product. So we have the map, our distribution for Hadoop, and it's got all the open source packages directly within it, but where we really innovate is in the course. So that's where we, we spent our time early on was really innovating that data platform to give everything within the Hadoop ecosystem, more reliability, better availability, performance, security scale, >>It's open source contributions to the court. And you guys put stuff on top of that, uh, >>And how it works. Yeah. And even some projects we lead the projects like with Apache Mahal and Apache drill, which is coming into beta shortly other projects, we commit and contribute back. But, um, so we take in the distribution, we're distributing all those projects, but where we really innovate is at that data platform level. So >>HP is a big data leader officer. They bought, uh, autonomy. They have HP Vertica. You guys are here. Hey, what are you doing here? Obviously we covered the cube, uh, the announcement with, uh, with, with HP Vertica, you here for that reason, is there other biz dev other activity going on other integration opportunities? >>Yeah, a few things. So, um, obviously the HP Vertica news was big. We went into general availability that solution the first week of may. So, um, what we have is the HP Vertica database integrated directly on top of our data platform. So it's this hybrid solution where you have full SQL database directly within your Hadoop distribution. Um, so it had a couple sessions on that. We had, uh, a nice panel discussion with our friends from Cloudera and Hortonworks. So really good discussion with HP about just the ecosystem and how it's evolving. The other things we're doing with HP now is, you know, we've got reference architectures on their hardware lines. So, um, you know, people can deploy Mapbox on the hardware of HP, but then also we're talking with the, um, the autonomy group about enterprise search and looking at a similar type of integration where you could have the search integrated directly into your Hadoop distro. And we've got some joint accounts we're piloting that she goes, now, >>You guys are integrating with HP pretty significantly that deals is working well. Absolutely. What's the coolest thing that you've seen with an HP that you can share. How so I asked you in the big data landscape, everyone's Bucher, you know, hunkering down, working on their feature, but outside in the real world, big data, it's not on the top of mind of the CIO, 24 7. It's probably an item that they're dressing. What have you seen and what have you been most impressed with at HP here? >>Yeah. Say, you know, this is my first HP event like this. I think the strategy they have is really good. I think in certain areas like the cloud in particular with the helium, I think they made a lot of early investments there and place some bets. And I think that's going to pay off well for them. And that marries pretty nicely with our strategy as well in terms of, you know, we have on-premise deployments, but we're also an OEM if you will, within Amazon web services. So we have a lot of agility in the cloud if you will. And I think as those products and the partnerships with HP, evolvable, we'll be playing a lot more with them in the cloud as well. >>I see that asks you a question. I want you to share with the folks out there in your own words, what is it about map bar that they may or may not understand or might not know about? Um, a little humble brag out there and share some, share some, uh, insight of, into, into map bar for folks that don't know you guys as a company and for the folks that may have a misperception of what you guys do shit share with them, with what, what map map is all about. >>Yeah. I mean, for me, I was in this space with Aster data and kind of the whole Hadoop and MapReduce area since 2008 and pretty familiar with everybody in the space. I really looked at Matt bars, the best technology hands down, you look at the Forrester wave and they rank us as having the best technology today, as well as product roadmap. I think the misperception is people think, oh, it's proprietary and close. It's actually the opposite of that. We have an unbiased open-source approach where we'll ship in support in our distribution, in the entire Apache spark stack. We're not selective over which projects within Apache spark. We support. Um, I feel like SQL on Hadoop. We support Impala as well as hive and other SQL on to do technologies, including the ability to integrate HP Vertica directly in the system. And it's because of the openness of our platform. I'd say it's actually more open because of the standards we've integrated into the data platform to support a lot of third-party tools directly within it. So there is no locked in the storage formats are all the same. The code that runs on top of the distribution from the projects is exactly the same. So you can build a project in hive or some other system, and you can port it between any of the distributions. So there isn't a, lock-in >>The end of the day, what the customers want is they want ease of integration. They want reliability. That's right. And so what are you guys working on next? What's the big, uh, product marketing roadmap that you can share with us? >>Yeah, I think for us, because of the innovations we did in the data platform allows us to support not only more applications, but more types of operational systems. So integrating things like fraud detection and recommendation engines directly with the analytical systems to really speed up that, um, accuracy and, and, uh, in targeting and detecting risk and things like that. So I think now over time, you know, Hadoop has sort of been this batch analytic type of platform, but the ability to converge operations and analytics in one system is really going to be enabled by technology like Matt BARR. >>How many employees do you guys have now? Uh, >>I'm not sure what our CFO would. Let me say that before. You can say we're over 200 at this point >>As well. And over five, the customers which got the data, you guys do summit graduations, we covered your relationship with HP during our big data SV. That was exciting. Good to see John Schroeder, big, very impressive team. I'm impressed with map. I will always have been. You guys have Stephanie kept your knitting saved. Are you going to do, and again, leading the big data space, um, and again, not proprietary is a very key word and that's really cool. So thanks for coming on. Like you really appreciate Steve. We'll be right back. This is the cube live in Las Vegas, extracting the city from the noise with map bar here at the HP discover 2014. We'll be right back here for the short break.

>>The queue at Hadoop summit, 2014 is brought to you by anchor sponsor Hortonworks. We do, I do. And headline sponsor when disco we make Hadoop invincible >>Okay. Welcome back. Everyone live here in Silicon valley in San Jose. This is a dupe summit. This is Silicon angle and Wiki bonds. The cube is our flagship program. We go out to the events and extract the signal to noise. I'm John barrier, the founder SiliconANGLE joins my cohost, Jeff Kelly, top big data analyst in the, in the community. Our next guest, Jack Norris, COO of map R security enterprise. That's the buzz of the show and it was the buzz of OpenStack summit. Another open source show. And here this year, you're just seeing move after, move at the moon, talking about a couple of critical issues. Enterprise grade Hadoop, Hortonworks announced a big acquisition when all in, as they said, and now cloud era follows suit with their news. Today, I, you sitting back saying, they're catching up to you guys. I mean, how do you look at that? I mean, cause you guys have that's the security stuff nailed down. So what Dan, >>You feel about that now? I think I'm, if you look at the kind of Hadoop market, it's definitely moving from a test experimental phase into a production phase. We've got tremendous customers across verticals that are doing some really interesting production use cases. And we recognized very early on that to really meet the needs of customers required some architectural innovation. So combining the open source ecosystem packages with some innovations underneath to really deliver high availability, data protection, disaster recovery features, security is part of that. But if you can't predict the PR protect the data, if you can't have multitenancy and separate workflows across the cluster, then it doesn't matter how secure it is. You know, you need those. >>I got to ask you a direct question since we're here at Hadoop summit, because we get this question all the time. Silicon lucky bond is so successful, but I just don't understand your business model without plates were free content and they have some underwriters. So you guys have been very successful yet. People aren't looking at map are as good at the quiet leader, like you doing your business, you're making money. Jeff. He had some numbers with us that in the Hindu community, about 20% are paying subscriptions. That's unlike your business model. So explain to the folks out there, the business model and specifically the traction because you have >>Customers. Yeah. Oh no, we've got, we've got over 500 paying customers. We've got at least $1 million customer in seven different verticals. So we've got breadth and depth and our business model is simple. We're an enterprise software company. That's looking at how to provide the best of open source as well as innovations underneath >>The most open distribution of Hadoop. But you add that value separately to that, right? So you're, it's not so much that you're proprietary at all. Right. Okay. >>You clarify that. Right. So if you look at, at this exciting ecosystem, Hadoop is fairly early in its life cycle. If it's a commoditization phase like Linux or, or relational database with my SQL open source, kind of equates the whole technology here at the beginning of this life cycle, early stages of the life cycle. There's some architectural innovations that are really required. If you look at Hadoop, it's an append only file system relying on Linux. And that really limits the types of operations. That types of use cases that you can do. What map ours done is provide some deep architectural innovations, provide complete read-write file systems to integrate data protection with snapshots and mirroring, et cetera. So there's a whole host of capabilities that make it easy to integrate enterprise secure and, and scale much better. Do you think, >>I feel like you were maybe a little early to the market in the sense that we heard Merv Adrian and his keynote this morning. Talk about, you know, it's about 10 years when you start to get these questions about security and governance and we're about nine years into Hadoop. Do you feel like maybe you guys were a little early and now you're at a tipping point, whereas these more, as more and more deployments get ready to go to production, this is going to be an area that's going to become increasingly important. >>I think, I think our timing has been spectacular because we, we kind of came out at a time when there was some customers that were really serious about Hadoop. We were able to work closely with them and prove our technology. And now as the market is just ramping, we're here with all of those features that they need. And what's a, what's an issue. Is that an incremental improvement to provide those kind of key features is not really possible if the underlying architecture isn't there and it's hard to provide, you know, online real-time capabilities in a underlying platform that's append only. So the, the HDFS layer written in Java, relying on the Linux file system is kind of the, the weak underbelly, if you will, of, of the ecosystem. There's a lot of, a lot of important developments happening yarn on top of it, a lot of really kind of exciting things. So we're actively participating in including Apache drill and on top of a complete read-write file system and integrated Hindu database. It just makes it all come to life. >>Yeah. I mean, those things on top are critical, but you know, it's, it's the underlying infrastructure that, you know, we asked, we keep on community about that. And what's the, what are the things that are really holding you back from Paducah and production and the, and the biggest challenge is they cited worth high availability, backup, and recovery and maintaining performance at scale. Those are the top three and that's kind of where Matt BARR has been focused, you know, since day one. >>So if you look at a major retailer, 2000 nodes and map bar 50 unique applications running on a single cluster on 10,000 jobs a day running on top of that, if you look at the Rubicon project, they recently went public a hundred million add actions, a hundred billion ad auctions a day. And on top of that platform, beats music that just got acquired for $3 billion. Basically it's the underlying map, our engine that allowed them to scale and personalize that music service. So there's a, there's a lot of proof points in terms of how quickly we scale the enterprise grade features that we provide and kind of the blending of deep predictive analytics in a batch environment with online capabilities. >>So I got to ask you about your go to market. I'll see Cloudera and Hortonworks have different business models. Just talk about that, but Cloudera got the massive funding. So you get this question all the time. What do you, how do you counter that army and the arms race? I think >>I just wrote an article in Forbes and he says cash is not a strategy. And I think that was, that was an excellent, excellent article. And he goes in and, you know, in this fast growing market, you know, an amount of money isn't necessarily translate to architectural innovations or speeding the development of that. This is a fairly fragmented ecosystem in terms of the stack that runs on top of it. There's no single application or single vendor that kind of drives value. So an acquisition strategy is >>So your field Salesforce has direct or indirect, both mixable. How do you handle the, because Cloudera has got feet on the street and every squirrel will find it, not if they're parked there, parking sales reps and SCS and all the enterprise accounts, you know, they're going to get the, squirrel's going to find a nut once in awhile. Yeah. And they're going to actually try to engage the clients. So, you know, I guess it is a strategy if they're deploying sales and marketing, right? So >>The beauty about that, and in fact, we're all in this together in terms of sharing an API and driving an ecosystem, it's not a fragmented market. You can start with one distribution and move to another, without recompiling or without doing any sort of changes. So it's a fairly open community. If this were a vendor lock-in or, you know, then spending money on brand, et cetera, would, would be important. Our focus is on the, so the sales execution of direct sales, yes, we have direct sales. We also have partners and it depends on the geographies as to what that percentage is. >>And John Schroeder on with the HP at fifth big data NYC has updated the HP relationship. >>Oh, excellent. In fact, we just launched our application gallery app gallery, make it very easy for administrators and developers and analysts to get access and understand what's available in the ecosystem. That's available directly on our website. And one of the featured applications there today is an integration with the map, our sandbox and HP Vertica. So you can get early access, try it and get the best of kind of enterprise grade SQL first, >>First Hadoop app store, basically. Yeah. If you want to call it that way. Right. So like >>Sure. Available, we launched with close to 30, 30 with, you know, a whole wave kind of following that. >>So talk a little bit about, you know, speaking of verdict and kind of the sequel on Hadoop. So, you know, there's a lot of talk about that. Some confusion about the different methods for applying SQL on predicts or map art takes an open approach. I know you'll support things like Impala from, from a competitor Cloudera, talk about that approach from a map arts perspective. >>So I guess our, our, our perspective is kind of unbiased open source. We don't try to pick and choose and dictate what's the right open source based on either our participation or some community involvement. And the reality is with multiple applications being run on the platform, there are different use cases that make difference, you know, make different sense. So whether it's a hive solution or, you know, drill drills available, or HP Vertica people have the choice. And it's part of, of a broad range of capabilities that you want to be able to run on the platform for your workflows, whether it's SQL access or a MapReduce or a spark framework shark, et cetera. >>So, yeah, I mean there is because there's so many different there's spark there's, you know, you can run HP Vertica, you've got Impala, you've got hive. And the stinger initiative is, is that whole kind of SQL on Hadoop ecosystem, still working itself out. Are we going to have this many options in a year or two years from now? Or are they complimentary and potentially, you know, each has its has its role. >>I think the major differences is kind of how it deals with the new data formats. Can it deal with self-describing data? Sources can leverage, Jason file does require a centralized metadata, and those are some of the perspectives and advantages say the Apache drill has to expand the data sets that are possible enabled data exploration without dependency on a, on an it administrator to define that, that metadata. >>So another, maybe not always as exciting, but taking workloads from existing systems, moving them to Hadoop is one of the ways that a lot of people get started with, to do whether associated transformation workloads or there's something in that vein. So I know you've announced a partnership with Syncsort and that's one of the things that they focus on is really making it as easy as possible to meet those. We'll talk a little bit about that partnership, why that makes sense for you and, and >>When your customer, I think it's a great proof point because we announced that partnership around mainframe offload, we have flipped comScore and experience in that, in that press release. And if you look at a workload on a mainframe going to duke, that that seems like that's a, that's really an oxymoron, but by having the capabilities that map R has and making that a system of record with that full high availability and that data protection, we're actually an option to offload from mainframe offload, from sand processing and provide a really cost effective, scalable alternative. And we've got customers that had, had tried to offload from the mainframe multiple times in the past, on successfully and have done it successfully with Mapbox. >>So talk a little bit more about kind of the broader partnership strategy. I mean, we're, we're here at Hadoop summit. Of course, Hortonworks talks a lot about their partnerships and kind of their reseller arrangements. Fedor. I seem to take a little bit more of a direct approach what's map R's approach to kind of partnering and, and as that relates to kind of resell arrangements and things like, >>I think the app gallery is probably a great proof point there. The strategy is, is an ecosystem approach. It's having a collection of tools and applications and management facilities as well as applications on top. So it's a very open strategy. We focus on making sure that we have open API APIs at that application layer, that it's very easy to get data in and out. And part of that architecture by presenting standard file system format, by allowing non Java applications to run directly on our platform to support standard database connections, ODBC, and JDBC, to provide database functionality. In addition to kind of this deep predictive analytics really it's about supporting the broadest set of applications on top of a single platform. What we're seeing in this kind of this, this modern architecture is data gravity matters. And the more processing you can do on a single platform, the better off you are, the more agile, the more competitive, right? >>So in terms of, so you're partnering with people like SAS, for example, to kind of bring some of the, some of the analytic capabilities into the platform. Can you kind of tell us a little bit about any >>Companies like SAS and revolution analytics and Skytree, and I mean, just a whole host of, of companies on the analytics side, as well as on the tools and visualization, et cetera. Yeah. >>Well, I mean, I, I bring up SAS because I think they, they get the fact that the, the whole data gravity situation is they've got it. They've got to go to where the data is and not have the data come to them. So, you know, I give them credit for kind of acknowledging that, that kind of big data truth ism, that it's >>All going to the data, not bringing the data >>To the computer. Jack talk about the success you had with the customers had some pretty impressive numbers talking about 500 customers, Merv agent. The garden was on with us earlier, essentially reiterating not mentioning that bar. He was just saying what you guys are doing is right where the puck is going. And some think the puck is not even there at the same rink, some other vendors. So I gotta give you props on that. So what I want you to talk about the success you have in specifically around where you're winning and where you're successful, you guys have struggled with, >>I need to improve on, yeah, there's a, there's a whole class of applications that I think Hadoop is enabling, which is about operations in analytics. It's taking this, this higher arrival rate machine generated data and doing analytics as it happens and then impacting the business. So whether it's fraud detection or recommendation engines, or, you know, supply chain applications using sensor data, it's happening very, very quickly. So a system that can tolerate and accept streaming data sources, it has real-time operations. That is 24 by seven and highly available is, is what really moves the needle. And that's the examples I used with, you know, add a Rubicon project and, you know, cable TV, >>The very outcome. What's the primary outcomes your clients want with your product? Is it stability? And the platform has enabled development. Is there a specific, is there an outcome that's consistent across all your wins? >>Well, the big picture, some of them are focused on revenues. Like how do we optimize revenue either? It's a new data source or it's a new application or it's existing application. We're exploding the dataset. Some of it's reducing costs. So they want to do things like a mainframe offload or data warehouse offload. And then there's some that are focused on risk mitigation. And if there's anything that they have in common it's, as they moved from kind of test and looked at production, it's the key capabilities that they have in enterprise systems today that they want to make sure they're in Hindu. So it's not, it's not anything new. It's just like, Hey, we've got SLS and I've got data protection policies, and I've got a disaster recovery procedure. And why can't I expect the same level of capabilities in Hindu that I have today in those other systems. >>It's a final question. Where are you guys heading this year? What's your key objectives. Obviously, you're getting these announcements as flurry of announcements, good success state of the company. How many employees were you guys at? Give us a quick update on the numbers. >>So, you know, we just reported this incredible momentum where we've tripled core growth year over year, we've added a tremendous amount of customers. We're over 500 now. So we're basically sticking to our knitting, focusing on the customers, elevating the proof points here. Some of the most significant customers we have in the telco and financial services and healthcare and, and retail area are, you know, view this as a strategic weapon view, this is a huge competitive advantage, and it's helping them impact their business. That's really spring our success. We've, you know, we're, we're growing at an incredible clip here and it's just, it's a great time to have made those calls and those investments early on and kind of reaping the benefits. >>It's. Now I've always said, when we, since the first Hadoop summit, when Hortonworks came out of Yahoo and this whole community kind of burst open, you had to duke world. Now Riley runs at it's a whole different vibe of itself. This was look at the developer vibe. So I got to ask you, and we would have been a big fan. I mean, everyone has enough beachhead to be successful, not about map arbors Hortonworks or cloud air. And this is why I always kind of smile when everyone goes, oh, Cloudera or Hortonworks. I mean, they're two different animals at this point. It would do different things. If you guys were over here, everyone has their quote, swim lanes or beachhead is not a lot of super competition. Do you think, or is it going to be this way for awhile? What's your fork at some? At what point do you see more competition? 10 years out? I mean, Merv was talking a 10 year horizon for innovation. >>I think that the more people learn and understand about Hadoop, the more they'll appreciate these kind of set of capabilities that matter in production and post-production, and it'll migrate earlier. And as we, you know, focus on more developer tools like our sandbox, so people can easily get experienced and understand kind of what map are, is. I think we'll start to see a lot more understanding and momentum. >>Awesome. Jack Norris here, inside the cube CMO, Matt BARR, a very successful enterprise grade, a duke player, a leader in the space. Thanks for coming on. We really appreciate it. Right back after the short break you're live in Silicon valley, I had dupe December, 2014, the right back.

>>I from Midtown Manhattan, the cute quiet coverage of big data NYC Civicon angled, Wiki bonds production made possible by Hortonworks. We do hairdo and lamb disco and new made invincible. And now your hosts, John furrier and Volante >>Hi buddy. We're back. This is Dave Volante with Jeff Kelly with Wiki bond. And this is the cube Silicon angle's continuous production. We're here at big data NYC right across the street from the Hilton where strata comp and a dupe world is going on. We've got a multi-time cube guest, Jack Norris, the CMO of map bars here, Jack. Welcome back to the cube first. So by the way, thank you so much for the support. As you know, we're across the street here at the Warwick hotel map, our, you guys have always been so generous supporting the cube. We can't thank you enough for that. So really appreciate it. Thank you. So we were able to listen to your keynote yesterday. It was, we, we, we weren't broadcasting, you know, head to head yesterday and had an opportunity to hear your keynote. So, first of all, how did that go? I want to ask you some questions about it. >>It, it was a really well-received and I think people were kind of clamoring to try to separate the myths from, from reality on, on Hadoop, >>We had three myths that you talked about, you know, one related to the distraction. I'd like to get into some of those. So what was the, the first myth was around the, the, the, the district distribution battle. So take us through that. >>So, you know, th the impression that it's a knock-down drag-out competitive battle across Hadoop distributions was the first myth. And the reality is that all of the distribution share the same open source Apache code. And this is one of the first markets that's really, really created, or the first open-source technologies it's really created a market. I mean, look, what's happened here with this whole, this whole big data and Hadoop, but given that early stage, there's the requirement to really combine that open source code with additional innovations to meet customer needs. And so what you see is you see those aggregators that are taken open source, you see others that are taking the open source, and then adding maybe management utility, couple of, of, you know, different applications on top. And then our approach at map R is we're taking the open source with those management innovations, doing some development, the open source community with things like Apache drill, and then really focusing on the underlying architecture, the data platform and providing innovations at that layer. So >>Actually sort of the three major destroys that we talk about all the time. You know, you guys, Hortonworks and Hadoop, you guys have been consistent the whole time as has Hortonworks, right? Cloud era basically put out a post recently saying, Hey, kind of going in a different direction, sort of what I call the tapped out of the Hadoop distro, you know, piece of it. But so there's a lot of discussion around it. You're putting forth the, Hey, it's not an internet seen war, but does it matter is my question? >>Well, I think if you take a step back, the Hadoop ecosystem is incredibly strong growing very, very quickly, fastest growing big data technology, one of the top 10 technologies overall. And I think it's because we are sharing the same API. It is possible for customers to learn on one, develop and move seamlessly to another. And, you know, in the keynote, I talked about the difference between the no SQL market, which is, you know, there is no consensus there and, and customers have to figure out not only what's the right word workload, but what's the technology that's actually going to have some staying power, right? >>That's a powerful comment. Amazon turn the data center and into an API, or you as the duke community is essentially turning data, access into an API. And that is a very powerful and leverageable concept. Okay. Your second myth was around the whole, no SQL yes. Piece of it. You help you put up a slide. I thought I read Jeff Kelly's reports. And I thought, I thought I knew them all, but there were a couple in there that I didn't recognize as you probably knew them all, but so take us through myth. Number two >>Too. I'm sure we missed some >>There wasn't room on the slide for anymore. >>The, yeah, it's basically about the consensus. There is no real consensus. There's no common API. There's no ability to move applications seamlessly across no SQL solutions. If you look at one no SQL solution, and that's, HBase a big inherent advantage because it's integrated with Hindu, you know, this whole trend is about compute and data together. So if you've got a no sequel solution, that's on that same, you know, massive data store, you know, big leg up. And, and then we got into the, well, if you've got HBase, it's included in all the distributions and all the distribution share the same open source, then obviously it must run the same across all distributions. And there, we shared some pretty interesting data to show the difference. When you, when you do architectural differences and innovations underneath that you can dramatically change the performance of, of not only MapReduce, but of no SQL. Yes. >>Okay. So not all no SQL is created equally. Not all HBase is created equally as essentially what you're saying there. Now the third piece was to dupe is enterprise ready, right? Yeah. So you guys were first to say, well, we have a Hadoop platform that's enterprise ready way ahead on that. Got criticized a lot for going down that path shrugged and said, okay, we'll just keep doing business with customers. And you've been again, very clear and consistent on that. So talk about the third myth >>And that's, you know, is, is Hadoop ready for prime time? And I think the way to combat that myth is by customer examples and showing the tremendous success that customers are enjoying with Hadoop. And, you know, we, we don't have time on the cube here to go through all of them, but, you know, I like to point out 90 billion auctions a day with Rubicon, they've surpassed Google in terms of ad reach. They're doing that on Mapbox 1.7 trillion events a month with comScore that's on, on map bar. You look in, in traditional enterprise, you know, a single retailer with over 2000 nodes of Hadoop. I mean, it's a key part of their merchandising and retail operations, and combining all sorts of, of data feeds and all sorts of use cases there, financial services over a thousand nodes of risk medication, personalized offers streamlining their operations. I mean, it's, it's dramatic. And then, you know, we shared some of the more, more interesting ones, esoteric ones like garbage and whiskey and weather prediction. >>There was consider these, we even as diverse and eclectic as they are, they consider these mission critical application. >>Oh, absolutely. No it it's. And I think that's the difference because what we're talking about is not Hadoop as this cash, right? This temporary processing, where we can do, you know, some interesting batch analytics and then take that and put that someplace else. And yes, there are applications like that, but companies soon realized that if I'm going to use this as a key part of my operations, and it's about data on compute, then I want a consistent permanent store. I want a system of record. So all of the SLS and high availability and data protection features that they expect in their enterprise applications should be present in Hadoop, right? That's where we focus. Let's run down a couple of those. >>What are some of the key capabilities that you need in an enterprise enterprise grade platform? That map bar is >>Well, let's, let's take, let's take business continuity cause that's important if you're really going to trust data there. And you know, one of the big drivers as you expand data is how much am I going to spend on it? And if you look at a large investment bank, $270 million of their budget, not total, but incremental to address the additional capacity, there's a big emphasis for let's look at a better way to do that. So instead of spending $15,000 a terabyte, if you can spend a few hundred dollars a terabyte, that's a huge, huge advantage. And that's the focus of Hindu, but to do that, well, then the features that are in this enterprise storage have to be present. And we're talking about, you know, mirroring and not a copy table function, but replication, that's how that's how organizations do it, right. If you're going to recovery and recovery, you know, you can't back up a petabyte of information through a copy function, right? You have to do a snapshot and the snapshots have to be consistent, right. And, and we're not saying anything that, you know, an enterprise administrator doesn't know, there is some confusion when you're more on the developer side as to what these features are and the difference between a fuzzy snapshot and a point in time, consistent snaps. >>Got it. So let's talk a little bit about the, the enterprise data hub, this, this concept that Michael Wilson with clutter introduced yesterday. Tell us a little bit about your take on, on, on Mike's I guess, definition and, and essentially I think trying to name the category of kind of what Hadoop can do and what, and where it sits in the architecture. Did you agree with his, his, >>Yeah. I mean, if you look at, at that description, it's about I'm taking important data and I'm putting it in a dupe and I'm combining a lot of different data sources and it's been referred to as a data lake and a data reservoir and a data ocean. I mean, we've heard a lot of terms. We worked with an outside consultant that was originally an architect at Terre data. It's been about eight months, almost a year ago now where he defined it and enterprise data hub. And it's it's, he went through kind of the list of requirements. And once you move from a transitory to a permanent store, then that becomes an enterprise data hub. And an enterprise data hub can be used to select and process information, maybe it's ETL and serve some downstream applications. It can also be useful to do analysis directly on it, to, you know, to serve different business functions. But the system requirements that he established for that I think are absolutely true. And it's, you have to have the full data protection. You have to have the full disaster recovery. You have to have the full high availability because this is going to be important data serving the organization. If it's data that you can lose, if it's data that you, you don't really care about having highly available, then it's a very narrow use case that that data hub serves. >>So you're saying the enterprise data hub isn't ready for prime time. >>No, I'm saying that there, there are requirements. And we have companies today that have deployed an enterprise data hub and they are quite successful with it. And, you know, the quotes are the ETL functions that they're doing on that hub are 10 times faster and it's 10 times cheaper than what they're seeing. >>Soundbite, Dave, >>I agree, but it's nuanced. Right. And so, you know, the customers cause a lot of vendors, right? They're all saying the same thing to the customers, right? So you've got your messaging that you've, you know, you've proven out over the last several years and then the entire market starts to use the same terminology. So it is, this is why I, like, I think this, what is, what are those >>Things? We're in a little bit of this, this kind of marketing fog here in the relative early stages. I think the best response there is customer proof points. And I think some education in the very beginning, you know, when they're in development and test, it's really important to understand, you know, what is Hadoop and what can I use it for and what data source am I going to leverage? I think the features that we're talking about really start to show up as you deploy in production. And as you expand its use in production and there we've enjoyed tremendous success, >>But he would argue that you have a lead in this space. I wouldn't, I don't think you would either the space being robustness enterprise ready, mission criticality is your lead increasing, decreasing staying the same. >>What's your sense? Well, it's hard cause there's no, you know, th th there's no external service that's out there, you know, interviewing every customer and, and giving numbers. I do know that we passed 500 paying customers. I do know that we've got significant deployments and you can measure those in terms of number of nodes, you know, in the thousands of nodes, you can measure those in terms of use cases. So we've got, you know, one company they've passed 20 different use cases on the same cluster. I think that's an interesting proof point. We're scaling in terms of the number of, of people in an organization that are trained in leveraging the data in map are again in the, in the thousands. So, you know, I think this market is so big and so dynamic that this isn't about, you know, one company success at the expense of everyone. Else's zero sum game. I think, you know, we're all here kind of raising this, this boat and focusing on this paradigm shift, but when it comes to production success, that's our focus. And I think that's where we've, we've proven that >>One thing I'm really want to get your opinion on, you know, as, as to do matures and some of the innovations you guys are doing and, and making the platform, you know, basically a multi application platform, you can do more things with Hadoop. And we've been talking about this on the cube, is that as that happens, you're going to start you as an industry. You're going to start bumping up against the EDW vendors and some of the other database vendors in the traditional world. And you're now you're doing some of the things that those, those tools can do now, you know, two years ago, it was very much just, this is all very complimentary Hadoop and your EDW. There's no overlap. We're gonna all play nice. But increasingly we're seeing that there is an overlap. How do you view that? Is that, and what is your relationship with those, with those EDW vendors and, and what are you hearing from customers when you go into a customer? Okay. >>So, I mean, there's a, there's a lot in that question. I think the F the first comment though, is don't look at Hadoop through this single data warehouse lens. And if you look at, at trying to use Hadoop to completely replace an enterprise data warehouse where there's, here's a few decades of experience, there, there are many organizations that have a lot of activities that are based in that data warehouse. And that's where we're seeing a data warehouse offload that is complimentary, but it gives organizations this lever to say, well, I'm going to control the fill rate, and I'm going to take some of the data that's no longer, you know, really active and put that on Hadoop and really change my ability to manage the costs in a data warehouse environment. The other thing that's interesting is that the types of applications that duper doing, I think are creating a new class it's about operations and analytics, kind of combined together, taking high arrival rate data and making very quick micro changes to optimize whether that's fraud detection or recommendation engines, or taking sensor data and predictive analytics for, for maintenance, et cetera. There is just a tremendous number of, of applications. In some cases, leveraging a new data source in some cases, doing new applications, but it's just opening things up. And, and I think organizations are moving to be very data-driven and Hadoop is at the center of that. >>And you control the field, right? That's another really good soundbites. And, and these that, you mentioned this high arrival rate data, this fraud detection, predictive analytics, maintenance, these are things that you're doing today with >>Navarre right? Yeah, >>Absolutely. Great. All right, Jack. Well, listen, always a pleasure. Thanks very much for coming by. Great to see you again. All right. Keep it right there about Uber, right back with our next guest. This is the cube we're live from the big apple.

>>Hi everybody. We're back. This is Dave Volante from Wiki bond.org. We're live at strata in Santa Clara, California. This is Silicon angle TVs, continuous coverage of the strata conference. So Riley media or Raleigh media is a great partner of ours. And thanks to them for allowing us to be here. We've been going all week cause it's day three for us. I'm here with Jeff Kelly Wiki bonds that lead big data analysts. And we're here with Jack Norris. Who's the VP of marketing at Matt bar Jack. Welcome to the cube. Thank you, Dave. Thanks very much for coming on. And you know, we've been going all week. You guys are a great sponsor of ours. Thank you for the support. We really appreciate it. How's the show going for you? >>Great. A lot of attention, a lot of focus, a lot of discussion about Hadoop and big data. >>Yeah. So you guys getting a lot of traffic. I mean, it says I hear this 2,500 people here up from 1400 last year. So that's >>Yeah, we've had like five, six people deep in the, in the booth. So I think there's a lot of, a lot of interests. There's interesting. >>You know, when we were here last year, when you looked at the, the infrastructure and the competitive landscape, there wasn't a lot going on and just a very short time, that's completely changed. And you guys have had your hand in that. So, so that's good. Competition is a good thing, right? And, and obviously customers want choice, but so we want to talk about that a little bit. We want to talk about map bar, the kind of problems you're solving. So why don't we start there? What is map are all about? And you've got your own distribution of, of, of enterprise Hadoop. You make it Hadoop enterprise ready? Let's start there. >>Okay. Yeah, I mean, we invested heavily in creating a alternative distribution one that took the best of the open source community with the best of the map, our innovations, and really it's, it's about making Hadoop more applicable, broader use cases, more mission, critical support, you know, being able to sit in and work in a lights out data center environment. >>Okay. So what was the problem that you set out to solve? Why, why do, why do we need another distribution of Hadoop? Let me ask it that way. Get nice and close to. >>So there, there are some just big issues with, with the duke. >>One of those issues, let's talk about that. There's >>Some ease of use issues. There's some deep dependability issues. There's some, some performance. So, you know, let's take those in order right now. If you look at some of the distributions, Apache Hadoop, great technology, but it requires a programmer, right? To get access to the data it's through the Hadoop API, you can't really see the data. So there's a lot of focus of, you know, what do I do once the data's in there opening that up, providing a full file based access, right? So I can look at it and treat it like enterprise storage, see the data, use my standard tools, standard commands, you know, drag and drop from a file browser. You can do that with Matt bar. You can't do that with other districts >>Talking about mountain HDFS as a NFS correct >>Example. Correct. And then, and then just the underlying storage services. The fact that it's append only instead of full random read-write, you know, causes some, some issues. So, you know, that's some of the, the ease of use features. There's a whole lot. We could discuss there. Big picture for reliability. Dependability is there's a single point of failure, multiple single points of failure within Hadoop. So you risk data loss. So people have looked at Hadoop. Traditionally is, is batch oriented. Scratchpad right. We were out to solve that, right? We want to make sure that you can use it for mission critical data, that you don't have a risk of a data loss that you've got full high availability. You've got the full data protection in terms of snapshots and mirroring that you would expect with the enterprise products. >>It gets back to when you guys were, you know, thinking about doing this. I'm not even sure you were at the company at the time, but you, your DNA was there and you're familiar with it. So you guys saw this big data movement. You saw this at duke moon and you said, okay, this is cool. It's going to be big. And it's gonna take a long time for the community to fix all these problems. We can fix them. Now let's go do that. Is that the general discussion? Yeah. >>You know, I think, I think the what's different about this. This is the first open source package. The first open source project that's created a market. If you look at the other open source, you know, Linux, my SQL, et cetera, it was really late in the life cycle of a product. Everyone knew what the features were. It was about, you know, giving an alternative choice, better Unix. Your, your, the focus is on innovation and our founders, you know, have deep enterprise background or CTO was at Google and charge of big table, understands MapReduce at scale, spent time as chief software architect at Spinnaker, which was kind of the fastest clustered Nazanin on the planet. So recognize that the underlying layers of Hadoop needed some rearchitecture and needed some deep investment and to do that effectively and do that quickly required a whole lot of focus. And we thought that was the best way to go to market. >>Talk about the early validation from customers. Obviously you guys didn't just do this in a vacuum, I presume. So you went out and talked to some customers. Yeah. >>What sorts of conversations with customers, why we're in stealth mode? We're probably the loudest stealth >>As you were nodding. And I mean, what were they telling you at the time? Yeah, please go do this. >>The, what we address weren't secrets. I there've been gyrus for open for four or five years on, on these issues. >>Yeah. But at the same time, Jack, you've got this, you got this purist community out there that says, I don't want to, I don't want to rip out HDFS. You know, I want it to be pure. What'd you, what'd you say to those guys, you just say, okay, thank you. We, we understand you're not a prospect. >>And I think, I think that, you know, duke has a huge amount of momentum. And I think a lot of that momentum is that there isn't any risks to adopting Hadoop, right? It's not like the fractured no SQL market where there's 122 different entrance, which one's going to win. Hadoop's got the ecosystem. So when you say pure, it's about the API APIs, it's about making sure that if I create a MapReduce job, it's going to run an Apache. It's going to run a map bar. It's going to run on the other distributions. That's where I think that the heat and the focus is now to do that. You also have to have innovation occurring up and down the stack that that provides choice and alternatives for. >>So when I'm talking about purists, I don't, I agree with you the whole lock-in thing, which is the elephant in the room here. People will worry about lock-in >>Pun intended. >>No, no, but good one good catch. But so, but you're basically saying, Hey, where we're no more locked in than cloud era. Right. I mean, they've got their own >>Actually. I think we're less because it's so easy to get data in and out with our NFS. That there's probably less so, >>So, and I'm gonna come back to that. But so for instance, many, when I, when I say peers, I mean some users in ISV, some guys we've had on here, we had an Abby Mehta from Triceda on the other day, for instance, he's one who said, I just don't have time to mess with that stuff and figure out all that API integration. I mean, there are people out there that just don't want to go that route. Okay. But, but you're saying I'm, I'm inferring this plenty who do right. >>And the, and by the API route, I want to make sure I understand what you're saying. You >>Talked about, Hey, it's all about the API integration. It's not >>About, it's not the, it it's about the API APIs being consistent, a hundred percent compatible. Right. So if I, you know, write a program, that's, that's going after HDFS and the HDFS API, I want to make sure that that'll run on other distributions. Right. >>And that's your promise. Yeah. Okay. All right. So now where I was going with this was th again, there are some peers to say, oh, I just don't want to mess with all that. Now let's talk about what that means to mess with all that. So comScore was a big, high profile case study for you guys. They, they were cloud era customer. They basically, in my understanding is a couple of days migrated from Cloudera to Mapbox. And the impetus was, let's talk about that. Why'd they do that >>Performance data protection, ease of use >>License fee issues. There was some license issues there as well, right? The, the, your, your maintenance pricing was more attractive. Is that true? Or >>I read more mainly about price performance and reliability, and, you know, they tested our stuff at work real well in a test environment, they put it in production environment. Didn't actually tell all their users, they had one guys debug the software for half a day because something was wrong. It finished so quickly. >>So, so it took him a couple of days to migrate and then boom, >>Boom. And they've, they handle about 30 billion objects a day. So there, you know, the use of that really high performance support for, for streaming data flows, you know, they're talking about, they're doing forecasts and insights into web behavior, and, you know, they w the earlier they can do that, the better off they are. So >>Greg, >>So talk about the implications of, of your approach in terms of the customer base. So I'm, I'm imagining that your customers are more, perhaps advanced than a lot of your typical Hadoop users who are just getting started tinkering with Hadoop. Is it fair to say, you know, your customers know what they want and they want performance and they want it now. And they're a little more advanced than perhaps some of the typical early adopters. >>We've got people to go to our website and download the free version. And some of them are just starting off and getting used to Hadoop, but we did specifically target those very experienced Hadoop users that, you know, we're kind of, you know, stubbing their toes on, on the issues. And so they're very receptive to the message of we've made it faster. We've made it more reliable, you know, we've, we've added a lot of ease of use to the, to the Hindu. >>So I found this, let me interrupt, go back to what I was saying before is I found this comment that I found online from Mike Brown comScore. Skipio I presume you mean, he said comScore's map our direct access NFS feature, which exposes a duke distributed file system data as NFS files can then be easily mounted, modified, or overwritten. So that's a data access simplification. You also said we could capitalize on the purchase of map bar with an annual maintenance charge versus a yearly cost per node. NFS allowed our enterprise systems to easily access the data in the cluster. So does that make sense to you that, that enterprise of that annual maintenance charge versus yearly cost per node? I didn't get that. >>Oh, I think he's talking about some, some organizations prefer to do a perpetual license versus a subscription model that's >>Oh, okay. So the traditional way of licensing software >>And that, that you have to do it basically reinforces the fact that we've really invested in have kind of a, a product, you know, orientation rather than just services on top of, of some opensource. >>Okay. So you go in, you license it and then yeah. Perpetual license. >>Then you can also start with the free edition that does all the performance NFS support kick the tires >>Before you buy it. Sorry. Sorry, Jeff. Sorry to interrupt. No, no problem >>At all. So another topic, a lot of interest is security making a dupe enterprise ready. One of the pillars, there is security, making sure access controls, for instance, making sure let's talk about how you guys approach that and maybe how you differentiate from some of the other vendors out there, or the other >>Full Kerberos support. We Lincoln to enterprise standards for access eldap, et cetera. We leveraged the Linux, Pam security, and we also provide volume control. So, you know, right now in Hindu in Apache to dupe other distributions, you put policies at the file level or the entire cluster. And we see many organizations having separate physical clusters because of that limitation, right? And we'd provide volume. So you can define a volume. And in that volume control, access control, administrative privileges data protection class, and, you know, in a sense kind of segregate that content. And that provides a lot of, a lot of control and a lot more, you know, security and protection and separation of data. >>That scenario, the comScore scenario, common where somebody's moving off an existing distribution onto a map are, or, or you more going, going, seeing demand from new customers that are saying, Hey, what's this big data thing I really want to get into it. How's it shake out there >>Right now? There's this huge pent up demand for these features. And we're seeing a lot of people that have run on other distributions switched to map our >>A little bit of everything. How about, can you talk a little bit about your, your channel? You go to market strategy, maybe even some of your ecosystem and partnerships in the little time. >>Sure. So EMC is a big partner of the EMC Greenplum Mr. Edition is basically a map R you can start with any of our additions and upgrade to that. Greenplum with just a licensed key that gives us worldwide service and support. It's been a great partnership. >>We hear a lot of proof of concepts out there >>For, yeah. And then it just hit the news news today about EMC's distribution, Mr. Distribution being available with UCS Cisco's ECS gear. So now that's further expanded the, the footprint that we have about. >>Okay. So you're the EMC relationship. Anything else that you can share with us? >>We have other announcements coming out and >>Then you want to pre-announce in the queue. >>Oops. Did I let that slip >>It's alive? So be careful. And so, in terms of your, your channel strategy, you guys mostly selling direct indirect combination, >>It's it? It, it's kind of an indirect model through these, these large partners with a direct assist. >>Yeah. Okay. So you guys come in and help evangelize. Yep. Excellent. All right. Do you have anything else before we gotta got a roll here? >>Yeah, I did wonder if you could talk a little bit about, you mentioned EMC Greenplum so there's a lot of talk about the data warehouse market, the MPB data warehouses, versus a Hadoop based on that relationship. I'm assuming that Matt BARR thinks well, they're certainly complimentary. Can you just touch on that? And, you know, as opposed to some who think, well, Hadoop is going to be the platform where we go, >>Well, th th there's just, I mean, if you look at the typical organization, they're just really trying to get their, excuse me, their arms around a lot of this machine generated content, this, you know, unstructured data that just growing like wildfire. So there's a lot of Paducah specific use cases that are being rolled out. They're also kind of data lakes, data, oceans, whatever you want to call it, large pools where that information is then being extracted and loaded into data warehouses for further analysis. And I think the big pivot there is if it's well understood what the issue is, you define the schema, then there's a whole host of, of data warehouse applications out there that can be deployed. But there's many things where you don't really understand that yet having to dupe where you don't need to find a schema a is a, is a big value, >>Jack, I'm sorry. We have to go run a couple of minutes behind. Thank you very much for coming on the cube. Great story. Good luck with everything. And sounds like things are really going well and market's heating up and you're in the right place at the right time. So thank you again. Thank you to Jeff. And we'll be right back everybody to the strata conference live in Santa Clara, California, right after this word from our.