>>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.