 It's, you know, what will that mean to my investment? And the announcement with Fusion I.O. is that, you know, we're 25 times faster on read-intensive HBASE applications, the combination. So, as organizations are deploying Hadoop and they're looking at technology changes coming down the pike, they can rest assured that they'll be able to take advantage of those in a much more aggressive fashion with MapR than other distributions. Jack, I got to ask you, we were talking last night at the Hadoop Summit, kind of the kickoff party and you know, everyone was there, all the top execs were there and all the developers. You know, we were on the queue, I think, I think, Dave or myself coined the term the big three of big data. You guys, Roms, Cloudera, MapR and Hortonworks really at the beginning, the key players early on and Charles from Cloudera was just recently on and he's like, oh no, Santa Prize grade stuff has been kicked around, it's been there from the beginning. You guys have been there from the beginning and MapR has never ever waffled on your messaging. You've always been very clear, hey, we're going to take Hadoop, open source Hadoop and turn it into an enterprise grade product. So that's clear, right? That's you, that's a great. So what's your take on this? Because now enterprise grade is kind of there, I guess the buzz around getting the folks that have crossed the chasm implemented. So what can you comment on that about one, enterprise grade, the reality of it? Certainly from your perspective you have opinion, but others, and then those folks that are now rolling it out for the first time, what can you share with them around what does it mean to be enterprise grade? So enterprise grade is more about the customer experience than a marketing claim. And by enterprise grade, what we're talking about are some of the capabilities and features that they've grown to expect in their other enterprise applications. The ability to meet full SLAs, full HA, recovery from multiple failures, rolling upgrades, data protection with consistent snapshots, business continuity with mirroring, the ability to share a cluster across multiple groups and have volumes. I mean there's a host of features that fall under the umbrella enterprise grade. And when you move from no support for any of those features to support to a few of them, I don't think that's going to HA, it's more like moving to low availability. And there's just a lot of differences in terms of when we say enterprise grade, what those features mean, versus what we view as kind of an incomplete story. So Describe, what do you mean by low availability? Well, I mean it's tongue-in-cheek, it's nice, it's a good term, as everybody's saying, just available when you, sometimes. Is that what you mean? It's not true availability, I mean availability is 99.9%. Right, right, so if you've got an HA solution that can't recover from multiple failures, that's downtime. If you've got an H base application that's running online and you have data that goes down and it takes 10 to 30 minutes to have the region servers recover it from another place in the distribution, that's downtime. If you have snapshots that aren't consistent across the cluster, that doesn't provide data protection. There's no point in time recovery for a cluster. So there's a lot of details underneath that, but what it amounts to is do you have interruptions? Do you have downtime? Do you have the potential for losing data? And our answer is you need a series of features that are hardened and proven to deliver that. What about recoverability, you mentioned that. You guys have done a lot of work in that area with snapshotting, that's kind of being kicked around. Are folks addressing, what are the, what's your competition doing in those areas of recoverability, you just mentioned availability, okay, got that, recoverability, security, compliance, and usability, those are the areas that seem to be the hot focus areas. What's going on in the industry, how would you give them the grade, the letter grade, if you will, candidly, compared to what you guys offer? Well, first of all, let's take recoverability. You know, one of the tendencies, you have a point in time recovery, the ability to restore to a previous point that's consistent across the cluster. And right now there's no point in time recovery for HDFS, for the files, and there's no point in time recovery for HBase tables. So there's snapshot support that's being talked about in the open source community with respect to snapshots, but it's being referred to in the JIRAs as fuzzy snapshots and really compared to copy table. So, Jack, I want to turn the conversation to the kind of the topic we've talked about before, kind of the open versus proprietary, that whole debate, we've heard about that, we talked about that before here on theCUBE. So just kind of reiterate for us your take, I mean, we hear perhaps because of the show where there's a lot of talk about the open sourced nature of Hadoop and some of the purists, as you might call them, are saying it's got to be open source, 100% Apache compatible, et cetera, and then there's others that are taking a different approach. Explain your approach and why you think that's the key way to really spur adoption of Hadoop and make it a enterprise-grade block block. We're a part of the community. We've got commitment going on, we've pioneered and pushed a Apache drill, but we have done innovations as well, and I think that those innovations are really required to support and extend the whole ecosystem. So Canonical distributes our M3 distribution, we've got all our packages are available on GitHub and open source, so it's not a binary debate. And I think the point being that there's companies that have jumped ahead and now the Peloton is pedaling faster and will catch up, will streamline, I think the difference is we re-architected, so we're basically in a race car and are racing ahead with enterprise-grade features that are required and there's a lot of work that still needs to be accomplished before that full re-architecture is in place. Well, I think for me, the proof is really in the pudding when it comes to talk about customers that are doing real things and real production grade, mission-critical applications that they're running. And to me, that shows the successor or relative success of a given approach. So I know you guys are working with companies like Ancestry.com, Live Nation, Quicken Loans. Maybe you could, could you walk us through a couple of those scenarios? Let's take Ancestry.com, obviously they've got a huge amount of data based on the kind of genealogical information. What are you guys doing with them? Yeah, so they've got, I mean, they've got the world's largest family genealogy services available on the web, so there's a massive amount of data that they make accessible and ability for analysis, and then they've rolled out new features and new applications, one of which is to ship a kid out and have people spit in a tube, return back, they do DNA matching and reveal additional details. So really some really fabulous leading edge things that are being done with the use of Hadoop. Interesting, so talk about when you went to work with them, what were some of their key requirements? Was it around, was it more around the enterprise grade, security and uptime kind of equation, or was it more around some of the analytics? Well, what's the kind of the killer use case for them? It's kind of a, you know, it's hard with a specific company or even to generalize across companies because they're really three main areas in terms of ease of use and administration, dependability, which includes the full HA, and then performance, and in some cases it's just one of those that kind of drives it and is used to justify, in other cases it's kind of a collection. The ease of use is being able to use a cluster not only as Hadoop, but to access it and treat it like enterprise storage. So it's a complete POSIX compliance file system underneath that allows the mounting and access and updates and using it in dynamic read-write. So what that means from an application level, it's faster, it's much easier to administer, and it's much easier and reliable for developers to utilize. Jack, I got to ask you about the marketing question because I see, you know, MAPR, you guys have done a good job. Marketing, certainly we want to be thankful to you guys as supporting theCUBE in the past, and you guys have been great supporters of our mission. But now the ecosystem's evolving, a lot more competition. Cloudera mentioned there's eight companies they're tracking in quote Hadoop, and certainly Jeff and I, and we're still going to be looking at, there's a lot more because Hadoop Washing has been going on now for the term Hadoop Washing, I mean jumping in and doing Hadoop, slapping that onto an existing solution. It's not been happening full bore for a year at least. What's the next for you guys to break above the noise? Obviously the communities are very active, projects are coming online, you guys have your mission in the enterprise, what's the strategy for you guys going forward? Is it more the same, anything new you want to share? Yeah, I think as far as breaking above the noise, it will be our customers, their success in their use cases that really put the spotlight on what the differences are in terms of using a big data platform. And I think what companies will start to realize is I'd rather analogy between supply chain and the big revolution in supply chain was focusing on inventory at each stage in the supply chain and how do you reduce that inventory level and how do you speed the flow of goods and the agility of a company for competitive advantage? And I think we're going to view data the same way. So companies instead of raw data that they're copying and moving across different silos if they're able to process data in place and send small results sets, they're going to be faster, more agile and more competitive. And that puts the spotlight on what data platform is out there that can support a broad set of applications and can have the broadest set of functionality. So what we're delivering is a mission grade, enterprise grade mission critical support platform that supports MapReduce and does that high performance, provides NFS, POSIX access, so you can use it like a file system, integrates enterprise grade, no SQL applications. So now you can do high speed, consistent performance, real time operations in addition to batch, streaming, integrated search, et cetera. So it's really exciting to provide that platform and have organizations transform what they're doing. How's the feedback going with Ted Dunning? I've been seeing a lot of buzz on the Twitter sphere. He's getting positive feedback here. He's a tech athlete. He's a guru. He's an expert. He's got his hands in all the pies. He's a scientist type. What's he up to? What's his role within MapR? And he's actually playing in the open source community. What's he up to these days? He's our chief application architect. He's on the leading edge of Mahalak, so machine learning. So sharing insights there. He was speaking at the Storm Meetup two nights ago and sharing how you can integrate long running batch predictive analytics with real time streaming and how the use of snapshots really makes that easy and possible. He travels the world and is helping organizations understand how they can take some very complex, long running processes and really simplify and shorten those. Had a chance to meet him in New York City at Last Duke World at a party and great guy. Fantastic geek and certainly he's doing great work and a shout out to Ted. Congratulations, continue up that support. How's everyone else doing? How's John and Trevis doing? How's the team at MapR? We're fiddling as fast as you can. We're busy growing, growing quickly. Now we're just shifting gears. We're beyond fiddling. Yeah, you're in the engine, you know. Give us an update on the company in terms of the growth and kind of where you guys are moving next. Yeah, we're expanding worldwide. Just this last few months we've opened up offices in London and Munich and Paris. We're expanding in Asia, Japan and Korea. So our sales and services and engineering and basically across the whole company continues to expand rapidly. Some really great, interesting partnerships and a lot of growth, not only as we add customers but it's nice to see customers that continue to really grow their use of MapR within their organization, both in terms of the amount of data that they're analyzing and the number of applications that they're bringing to bear on the platform. Talk about that a little bit because I think one of the trends we do see is when a company brings in big data, a big data platform, they might start experimenting with it, build an application and then maybe in the marketing department and then the sales guys see it and they say, well maybe we can do something with that. Is that typically the kind of the experience you're seeing and how do you support companies that want to start expanding beyond those initial use cases to support other departments, potentially even other physical locations around the world? How do you kind of support that? The beauty of that is if you have a platform that can support those new applications, so if mission critical workloads are not an issue, if you support volume so that you can logically separate, makes it much easier, which we have. So one of our customers, Zion's Bank, they brought in MapR to do fraud detection and pretty soon, the fact that they were able to collect all of that data, they had other departments coming to them and saying, hey, we'd like to use that to do analysis on because we're not getting that data from our existing systems. Yeah, they come in and you're sitting on a gold mine there for some of our use cases. You also mentioned as you're expanding internationally, what did you take on the international market for big data to do specifically? Is the US kind of leaps and bounds ahead of the rest of the world in terms of adoption of the technology? What are you seeing out there in terms of where the rest of the world stands? I wouldn't say leaps and bounds. And I think internationally they're able to maybe skip some of the experimental steps. So we're seeing deployment across financial services and telecom and it's fairly broad recruit technologies. They're the largest provider of recruiting services. Indeed.com is one of their subsidiaries. They're doing a lot with Hadoop and MapR specifically. So it's been expanding rapidly. Fantastic. So also, you think about Europe, what's going on with Google and some of the privacy concerns, even here I should say. Is there different regulatory environments you've got to navigate when you're talking about data and how you use data when you're starting to expand to other locales? Yeah, there's typically bi-vertical. There's different requirements, HIPAA and healthcare and Basel II and financial services. And so all of those, it basically it's the same theme of when you're bringing Hadoop into an organization and into a data center, the same sorts of concerns and requirements and privacy that you're applying in other areas will be applied on Hadoop. Now kind of turning back to the technology you mentioned Apache drill, I'd love to get an update on kind of where that stands. You know, and put that into context for people. I hear a lot about the SQL on Hadoop question here. Where does drill fit into that equation? Well, there's a lot of different approaches to provide SQL access. A lot of that is driven by how do you leverage some of the talent and organization that speaks SQL. So there's developments with respect to Hive, there's other projects out there. Apache drill is an open source project getting a lot of community involvement and the design center there is pretty interesting. It started from the beginning as an open source project and two main differences. One was in looking at supporting SQL, let's do full ANSI SQL. So it's full 2003 ANSI SQL, not a SQL like. And that'll support the greatest number of applications and you know, avoid a lot of support and issues. And the second design center is let's support a broad set of data sources. So nested sources like JSON, Scheme on Discovery, it's basically fitting it into an enterprise environment where sometimes it's kind of messy and can get messy as acquisitions happen, et cetera. So it's complimentary, it's about enabling interactive low latency queries. Jack, I want to give you the final word. We're out of time. Thanks for coming on theCUBE. Really appreciate you. Great to see you again, CUBE alumni. But final word and we'll end the segment here on theCUBE is your quick thoughts on what's happening here at Hadoop World. What is this show about? Share with the audience what's the vibe, the summary, quick sound bite on Hadoop. Yeah, I think I'll go back to how we started. It's not if you use Hadoop, it's how you use Hadoop and look at not only the first application but what it's going to look like in multiple applications and pay attention to what enterprise grade means. Okay, there was theCUBE. We got more coverage coming. Jack Norris with MapR. Obviously one of the original big three still on the list in our mind. And the market's mind with a unique approach to Hadoop and they've been doing great. This is theCUBE. I'm John Furrier with Jeff Kelley. I'll be right back after this short break. I don't quite a gap in tech news. There are plenty of tech shows that provide new gadgets and talk about the low latency. Those shows are pretty...