 Live from Boston, Massachusetts, it's theCube, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. Welcome back to Boston, everybody. This is theCube, the leader in live tech coverage. We're here in Boston at Snowy Boston. This is Spark Summit. Spark Summit does an East Coast version. They do a West Coast version. They've got one in Europe this year. TheCube has been a partner with Databricks as the live broadcast partner. Our friend, Bill Peterson is here. He's the head of partner marketing at MapR. Bill, good to see you again. Thank you, thanks for having me. So, how's the show going for you? Give us the vibe, we're kind of winding down day two. It is, the show's been great. We've got a lot of traffic coming by, a lot of deep technical questions, which is, you know- Hard core. It is, which is, you know, so I spend a lot of time there smiling and going, yeah, talk to him. But it's great, you know, we're getting those deep technical questions and it's great. We actually just got one on Luster, which, you know, I had to think for a minute. Oh, HPC, right? Yeah. It was like way back in there. I don't craze on the floor. Yeah, that's true. But a lot of our customers as well, you know, so United Health Group, Wells Fargo, AMEX coming by, which is great to see them and talk to them, but also they've got some deep technical questions for us. So it's moving the needle with existing customers, but also new business, which is great. So I can ask a basic question. Yeah. What is MAPR, right? MAPR started in the early days, the Hadoop Distro vendor, one of the big three. When somebody says to you, what is MAPR? What do you say? My answer today is MAPR is an enterprise software company that delivers a converged data platform. That converged data platform consists of a file system, a NoSQL database, a Hadoop distribution, a Spark distribution, and a set of data management tools. And as a customer of MAPR, you get all of those. You can turn them all on if you'd like. You can just turn on the file system, for example, if you wanted to just use the file system for storage. But the enterprise software piece of that is all the hardening we do behind the scenes on things like snapshots, mirroring, data governance, multi-tenancy, ease of use, performance, all of that baked into the solution or the platform as we're calling it now. So as you were kind of alluding to, a year ago now, we kind of got out of that business of saying, okay, we're, you know, lead 100% with Hadoop, and then while we have your attention, or if we don't, oh, hey, we got all this other stuff in the basket we want to show you. We went the platform play and said, we're going to include everything, and it's all there, and then the baseline underneath is the hardening of it, you know, the file system, the database, and the streaming product, actually, which I didn't mention, which is kind of the core, and everything plays off of there. And that, honestly, has been really well-received, and it just, I feel, makes it so much easier because it happened here, we get the question, okay, how are you different from Cloudera or Hortonworks? And some of it here, given the nature of the attendees, is very technical, but there's been a couple of business users that I've talked to, and when I talk about us as an enterprise software company delivering a plethora of solutions versus just Hadoop, you can see the light going on sometimes in people's eyes, and I got it today earlier. I had no idea you had a file system, which to me just drives me insane because the file system is pretty cool, right? Well, and you guys are early on and investing in that file system and recovery capabilities and all the... Two years in stealth writing it. Nasty, gnarly, hard stuff that was kind of poo-pooed early on, but MapR was never patient about waiting for the sort of open source community to just figure it out and catch up. You always just said, all right, we're going to solve this problem and go sell. And I'm glad you said that. I want to be clear, we're not given up on open source or anything, right? That's, open source is still a big piece. 50% of our engineer's time is working on open source projects, right? That's still super important to us. And then back in November-ish last year, we announced the MapR ecosystem packs, which is our effort to help our customers that are using open source components to stay current, right? Because that's a pain in the butt, right? So this is a set of packages that have a whole bunch of components. We lead with Spark and Drill, and that was by customer requests that they were having a hard time keeping current with Spark and Drill. So the packs allow them to come up to current level within the Converge data platform for all of their open source components. And that's something we're going to do dot levels. So I think we're at 2.1 or 2 now. The dot levels will bring you up on everything. And then the big ones like the 3.0s, the 4.0s will bring Spark and Drill current. And so we're going to kind of leapfrog those. So that's still a really important part of our business, and we don't want to forget that part. But what we're trying here to do is via the platform is deliver all of that in one entity, right? So yeah, the Converge data platform is relevant, presumably because you've got the history of Hadoop, because you've got all these different components, and you've got a cobbling together, and there are different interfaces, and different environments, you're trying to unify that, and you have unified that, right? So what is your customer feedback with regard to the Converge data platform? Yeah, so it's a great question because for existing customers, it was like, thank you, right? It was one of those, right? Because we're listening. Actually, again, glad you said that. This week in addition to Spark Summit, we're doing our yearly customer advisory board. So we've got like a lot of vendors, we've got a 30 plus company customer advisory board that we bring in and we sit down with them for a couple of days and they give us feedback on what we should and shouldn't be doing and where directional and all that, which is super important. And that's where a lot of this Converge data platform came out of is the need for, you know, there's just too much. It's kind of confusing. We would, and then, you know, I'll give the example of Streams, right? We came out with our streaming product last year and, you know, okay, you've got, I'm using Hadoop, I'm using your file system, I'm using NoSQL. Now you're adding Streams. This is great, but you know, now like MAP, right, the ecosystem packages, I have to keep everything current. You know, this is, you got to make it easier for me. You got to make my life easier for me. So for existing customers, it's a stay current. I like this, you know, the model. I can turn on and off what I want, when I want, great, great model for them. And then for existing business, for new business, it gets us out of that Hadoop only mode, right? I kind of joking, they call us Hadoop plus, plus, plus, plus, right? We keep adding solutions and add it to a single cohesive data platform that we keep updated. And as I mentioned here, you know, talking to new customers or new prospects or a potential new business, when I describe the model, you can just see the light going on and they realize, wow, there's a lot more to this than I had imagined. I thought you were, you know, I got it earlier today. I thought you guys only did Hadoop, you know, which is a little infuriating as a marketer, but I think we're from a mechanism and a delivery and a message and a story point of view. It's really helped. More cube time will help, Dennis. Well played, well played. Okay, so Spark comes along a couple of years ago and it was like, oh, what's going to happen to it? Okay, so you guys embrace Spark, talk more specifically about Spark, where it fits in your platform and the ecosystem generally. So, you know, Spark, Hadoop, others, as a entity to bring data into the converged data platform. Right, that's one way to think about it, right? Way oversimplified, obviously, but that's a really great way, I think, to think about it is if we're going to provide this platform that anybody can query on, you can run analytics against, you know, we talk a lot about now converged applications. So taking historical data, taking operational data, so streaming data, right? Great example, putting those together and you could say use the data lake example if you want, that's fine, but putting them into a converged application in the middle where they overlap, right? Kind of typical Venn diagram where they overlap and that middle part is the converged application. What's feeding that? Well, Spark could be feeding that, Hadoop could be feeding that. Just yesterday we announced a container, a Docker, right, for containers. That could be feeding into the converged data platform as well, so we look at all of these things as an opportunity for us to manage data and to make data accessible at the enterprise level. And then that enterprise level goes back to what I was talking before. It's got to have all of those things like multi-tenancy and snapshots and mirroring data. Excuse me, data governance, security, et cetera. But Spark is a big component of that, right? You know, all of the customers that came by here that I mentioned earlier, which are some really good names for us, are all using Spark to drive data into the converged data platform. So we look at it as we can help them build new applications within converged data platform with that data. So whether it's Spark data, Hadoop data, container data, we don't really care. So along those lines, if the focus of intense interest right now is on Spark, and Spark says, oh, and we work with all these databases, data stores, file systems, if you approach a customer who's Spark first, you know, what's the message relative to all the other data stores that they can get to that are getting too techy, their API? Sure, sure. Well, I think as you know, George, we support a whole bunch of APIs, right? So I guess for us, it's the breadth, right? I'm thinking of Spark in particular. If someone says, I want to run, specifically, I guess, I want to run Databricks, but I need something underneath it to capture the data and to manage it. Well, I think that's the beauty of our file system there, right? You know, we, as I mentioned, you know, if you think about it from an architectural point of view, our file system along the bottom, or it could be our database or a streaming problem, in this instance, which- That's what I'm getting into all three. Yeah, picture that as the bottom layer as your storage, I shouldn't say storage layer, but as the bottom layer, right? Because it's not just storage, right? It's more than storage, right? Middle layer is maybe some of your open source tools and the like. And then above that is what I call your data delivery mechanisms, right? Which would be Spark, for example, one bucket, another bucket could be Hadoop, and another bucket could be these microservices we're talking about. I mean, let me draw the picture in another way using a partner, right, SAP, right? One of the things we've had some success with SAP is SAP HANA sitting up here, right? SAP would love to have you put all your data in HANA, right? It's probably not going to happen, right? Yeah, good luck, right? But what if you, hey customer, what if you put zero to two years' worth of data, historical data in HANA? Okay, maybe the customer starts nodding their head like you just did. Hey, customer, what if you put two to five years' worth of data in business warehouse? Guess what, you already own that, right? You've been an SAP customer for a while, you already have it. Okay, the customer is now really nodding their head, you got their attention, and this to your original question, whether it's Spark or whatever, right? Five plus years, put a map up. Oh, and then like Canivora could do the query. We can query, you drill and query across all of them, oh, including the business, oh right, including the business warehouse. So, running in the file system. So, that to me, and we do this obviously with our joint SAP MapR customers, that to me is kind of a really cool vision. And to your original question, if that was Spark at the top feeding it rather than SAP, sure, right, why not? Okay. What can you share with us Bill about business metrics around MapR? However you choose to share it, headcount. Yeah, yeah. You want to give us gross margins by product, that's great. Actually, we know they're very high. Would you like revenues too? We know they're very high because you're a software company, so that's actually a bad question. Operating profit by profit. You don't have to give us top line revenues, maybe. What are you guys saying publicly about the company, its growth, that's, you know, give us the latest? So, it's fantastic, right, number one, right? Hiring like crazy, we're well north of 500 people now. I actually, you want to hear a funny story. I, yesterday, was texting in the booth with a candidate from my team back and forth on salary. Did the negotiation, salary negotiation on text right there in the booth and closed her. She starts on the 27th, so I'm very excited about that. So, moving along on that, 700, 800 plus customers as we, you know, we talk about, we will be doing our, we just finished our fiscal year on January 31st, so we're on a Feb 1, right, fiscal year. And we always do a momentum press release, you know, which will be coming out soon. But everything, you know, we've hiring again, like crazy as I mentioned, executive staff is all filled in and, you know, built to scale, which we're really excited about. You know, we talk a lot about, you know, the kind of uptake of, you know, used to be of the file system, Hadoop, et cetera, on its own. But now in this one, the momentum release will be done, we'll talk about the coverage data platform and the uplift we've seen from that. So, you know, we obviously can't talk revenue numbers and the like, but everything, David, I got to tell you, you know, we've been doing this a long time. All of that is just all moving in the right direction. And then the other example I'll give you from my world in the partner world, you know, last year I rebranded our partner to the Converged Partner Program, right, when we were going with this whole Converged thing, right? And we established three levels, elite, preferred and affiliate with different levels there. But there also, there's revenue at each, you know, revenue requirements at each level, so elite, preferred and affiliate. And, you know, there's resell and influence revenues. We have MDF funds not only from the big guys coming to us, but we're paying out MDF funds now to select partners as well. So all of this stuff I always talk about is the maturity of the company, right? We're maturing in our messaging, we're maturing in the level of people who are joining and we're maturing in the customers and the deals, you know, the deal sizes and volumes that we're seeing. So it's all moving in the right direction. Great, awesome, congratulations. Thank you, yeah, I'm excited. Can you talk about number of customers or number of employees relative to last year? Well, boy. I don't, honestly, George, I don't know off the top of my head. I apologize, I don't know the metric, but I know it's north of 500 today of employees and it's like 7,800 customers, yeah, yeah. And a little bit more on this partner, elite, preferred and affiliate. So, and you said, what did you call it? Converged partners? Yeah, yeah. So what are some of the details of that? Sure, so the elites are invite only, right? And those are some of the bigger ones, right? So for us, we're in- Some examples. Cisco, SAP, AWS, you know, others, but those are some of the big ones. And there we're looking at things like resell and influence revenue, right? We're track, that's how I, alright, that's what I track in my, I always jokingly say at MapR, even though we're kind of a big startup now, I always jokingly say at MapR, you have three jobs, right? You have the job you are hired for, you have your Thursday night job and you have your Sunday night job, right? So in the job I was hired for, partner marketing, I track influence and resell revenue, right? So at the elite level, we're doing both, right? We're like, Cisco resells us, right? So this S series, right? They were in their SKU, their sales reps can go sell an S series for big data workloads or analytical workloads, MapR on it, off you go, right? And our job then is caching checks, which I like. That's a good job to have in this business, right? At the preferred level, it's kind of that next tier of big players, but they haven't, revenue thresholds haven't moved into the elite yet. Customer, customers, partners in there are like the micro strategies of the world, we're doing a lot with them, Tableau, Tal, and a lot of the BI vendors in there. And then the affiliates are kind of the smaller guys who maybe will do one piece of a campaign during the year with them. So like, I'll give you an example, Attunity, you guys know those guys right here? Last year, we were doing a campaign on DWO, Data Warehouse Offload, right? We wanted to bring them in, but this was a MapR campaign running for a quarter, right? And we're typical like a lot of companies, we run four campaigns a year, and then my partner in field stuff kind of ops into that and we run stuff to support it. And then corporate marketing does something pretty traditional, but what I try and do is pull these partners into those campaigns. So we did a webinar with Attunity as part of that campaign. So at the affiliate level, the lower level, we're not doing a full go to market like we would with the elites at the top, but they're being brought into our campaigns and then obviously hopefully we hope on the other side they're going to pull us in as well. Great, last question. Sure, what should we pay attention to? What's coming up? Let's see, we got some events, we got Strada coming up. You'll be out your way, right? Or MapR way? As my Twitter handle says, C-11A, right? That's where I am. Yeah, I mean the Docker announcement we're really excited about and microservices, right? You'll see more from us on the whole microservices thing. Streaming is still a big one we think for this year. You guys probably agree. That's why we announced the MapR streaming product last year. So again, from a go to market point of view and kind of putting some meat behind streaming with part, not only MapR, but with partners. So streaming as a component and a delivery model for managing data in CDP. I think that's a big one. Machine learning is something that we're seeing more and more kind of touching us from a number of customers but also from the partner perspective. So I see all the partner requests that come in to join the partner program and there's been an uptick in the machine learning customers that want to come in and, excuse me, partners that want to be talking to us, which I think is really interesting. Where you would be the sort of prediction serving layer. Exactly, or a data store, right? A lot of them are looking for just an easy data store that the MapR file system can do. Infrastructure to support that. You know, commodity, right? The whole old promise of Hadoop or just a generic file system is, give me easy access to storage on commodity hardware. The machine learning, so what the machine learning, the new, or I guess the existing machine learning vendors need an answer for that, right? When the customer asks them, they want just an easy answer. So we just use MapR FS for that and we're done. Okay, that's fine with me, I'll take that one. So that's the operational end of that machine learning pipeline that we call DevOps for data science. Correct, right. And I mean, I guess the nice synergy there is the whole going back to the Docker microservices one, right? There's a DevOps component there as well. So might be interesting marrying those together. Okay. All right, we got to go. Bill, thanks very much. Thanks guys. All right, thank you. All right, George and I will be back to wrap. We're going to do part two of our big data forecast right now, so stay with us right back.