 Live from New York City, it's The Cube. Here is your host, Dave Vellante. Hi everybody, this is Dave Vellante and you're watching The Cube. We are on the ground at the USS Intrepid at the MAPR party. Jabari Norton is here. He runs strategic alliances for MAPR. Jabari, thanks for coming on. Absolutely, thanks. So what's happening? Where are we in the state of big data? What's happening? Making deals? Making alliances? You've been busy. We've been really busy here. You know, we've got a lot going on with Cisco systems. That's part of one of the partners that we have here tonight. But lots of different partners actually on the ecosystem. It's very funny. I've been at MAPR now for three years and it's amazing to kind of see what the amount of traction that we've got. We're still early innings with where we got, but we've got a long way to go. So I've got to ask you, so everybody's doing deals. Everybody's, you know, inking contracts, press releases. How do you differentiate? How do we, as observers, differentiate what we sometimes call the Barney deals? You know what I mean? The deals that, you know, don't really have a lot of teeth from those ones that actually produce value for customers and obviously for the suppliers as well. How do you do that? How do you choose and pick? Well, really, you're looking for business outcomes, right? That's what you're really looking for. You have lots of deals that are in early stages. People kicking the tires, et cetera. You want to find those deals that actually live in results to the end users that are delivering business outcomes. With a dupe, you can download it and kind of figure it out from there. What we really try and do is to try and go in and find those deals where that's going to make an impact to the customer, even driving top line revenue, or reducing costs. And that's really where MapR has a differentiated solution and drives those production-based applications. So talk a little bit about that differentiation, right? Because you've got tons of action, a lot of competition. How do you position MapR? How do you differentiate, you know, what are your strengths relative to others? And specifically, what do you emphasize when you're doing, you know, partnerships? Well, it really comes down to three big things. And we've been preaching this for three years in the six years that MapR has been around. One is we make a dupe easy. Easier to use, easier to integrate with those applications, too. Much more dependable. So enterprise-grade, high availability. What you would expect from an enterprise-class solution and performance and fast. So delivering much faster, greater TCO to customers. And that performance you would expect from a world-class enterprise Hadoop distribution. So we talk to a lot of practitioners, and they tell us, you know, Hadoop's hard, right? The guys at the left side of the bell curve are doing a great job. They've got the skill sets, they've got the data scientists. But the fat middle, you know, they're really struggling. They've got a hard time. Talk a little bit more about how you simplify Hadoop, how you make it easier. Maybe even some examples of partnerships that you bring to the table to make things easier. So really what we do is we bring Hadoop to the masses, and what we do there. You know, our partnerships that we have with Cisco, where you're actually bringing a full integrated stack there, where you're bringing in third-party solutions, whether it be in Informatica, SaaS, et cetera, bringing them all together. So customers can have a full stack solution and can deliver true business outcomes. While Hadoop is the platform, we really need those applications that sit up on the top layer above that to bring it all together from the customer. So that ecosystem and those partners that we're really building are driving tremendous value to our customers. So we get this question a lot from our practitioners in the Wikibon community. They say, is my data warehouse a dinosaur? What should we tell them? You know, I wouldn't say it's a dinosaur, but it's something that you actually have. So what you need to do is really analyze what's in that. What are the right data types to be sitting in that data warehouse? If there's newer data types, unstructured data types that aren't really designed for that data warehouse, put those in Hadoop, those data sets, those structured data sets, some of those data sets that are designed for the data warehouse, keep those there and you can kind of have a tiered base data platform where you can run some of your apps in the data warehouse as well as some of your apps in Hadoop. Okay, so you see the data warehouse, the traditional data warehouse is a fundamental part of a customer's big data strategy. Is that right? Yeah, nobody's throwing anything out in the data center. They've made way too much investment. There's way too many applications that are there. You have to kind of co-mingle with actually what's there and really kind of take the best of all the different technologies and drive that business value that's actually there. Hadoop brings that commodity level clustered based distribution of storing tons of data from an unstructured standpoint and bringing that to the max. So from a tooling standpoint, I got to believe data integration is like top of the list. You mentioned Informatica. SyncSort is one of your sponsors here. Atunity is another company, you know, working on that. They're out of talent as well. I mean, that seems to me to be critical. Do customers understand that? Do they? I mean, you see a lot of big data projects spinning up. Is anybody paying attention to data quality, data governance, data integration from a strategic perspective? Or is it more just sort of bespoke projects that ultimately somebody's going to have to clean up? What do you think about that? So it goes back to your force point, right? Where MapR plays is really is more of those production grade distributions, right? When you're kind of going into production, then you're really getting those enterprise grade qualities. That's when those third-party ecosystem projects are really important, right? When you're in the early stages and you're just kicking the tires, nobody really thinks about those things. When you're putting in that production, those other solutions really matter. And that's where our partners really come into play. And that's where MapR delivers a tremendous amount of value. If you, I mean, you talk to a lot of customers. Obviously, you as MapR are a company and you personally with some of your partners. Where do you think we are? If you had to take a sort of random sample of customers, what percent do you think are actually in production with Hadoop? And this is a little biased because you guys probably have more as a percentage in production. But what do you think as a sampling across? Let's take North America. As a sampling across North America, what percent of the customers would you say have Hadoop in production today? I would say it's probably in the 5% or 10% range. If you kind of look at Gardner and what foresters say it's in the 5% or 10% range, when you look at MapR customers, one of the 70 to 80% range, really we give those enterprise gear capabilities, we make it much easier to use. So if you're a large enterprise looking to deploy this, we provide to you those enterprise class features that are there day one and the ability to do that. So we're still early in that production and option curve, but it's moving extremely fast in that direction. So you think, you know, we like to talk in funnels, right? You know, any sales person wants to talk in funnels. So the top of the funnel is guys that are, you know, thinking about big data that, you know, Hadoop is going to be, you know, considering it as part of their data strategy, which is kind of everybody. Absolutely. And that's a big chunk of the world. And then you've got the guys that are sort of kicking the tires, doing some science experiments, and you've got the guys in production. How does MapR sort of work that funnel with this partnership to make sure that they're coming into your world and not going to your competitors? Yes. So we have the funnel in the top where they're kind of coming down and coming in, you know, going from kicking the tires into production. We actually see an hourglass effect in the bottom end. Once we get those first production use cases in, they expand rapidly. We're seeing 3x, 5x, 10x production increases of use cases and cluster sizes. Actually, after they put those first use cases in, so we really see that kind of concave effect. So they come in the top, they kind of get narrow and they really expand out once they get in the production. So that's interesting. So we were talking about the Enterprise Data Warehouse before, but would you agree that the Enterprise Data Warehouse spending patterns are shifting? That people are maybe rethinking where they're going to put their investments. They're maybe putting some R&D money or maybe more production money into Hadoop and maybe baselining their Enterprise Data Warehouse. I agree with you. It's a fundamental part of their strategy, but it's not sucking up the budget, right? Would you agree with that? I would definitely agree with that. So where do you think that goes over time? Is the Hadoop tail going to eventually become the dog? I think it will, right? I mean, I think it's going to take some time, right? It's not going to be something you do overnight, but I think as customer courtesy of really the value, the production grade quality that you can get out of distributions like MapR, etc., you're going to see that tail starting to wag the dog in the near future. Jibar, I love your enthusiasm. You guys got some momentum. Thanks very much This is Dave Vellante and you're watching The Cube.