>> Narrator: From San Jose, California, in the heart of Silicon Valley, it's theCUBE. Covering Hadoop Summit 2016. Brought to you by Hortonworks. Here's your host, John Furrier. >> Welcome back everyone. We are here live in Silicon Valley at Hadoop Summit 2016, actually San Jose. This is theCUBE, our flagship program. We go out to the events and extract the signal to the noise. Our next guest, David Richards, CEO of WANdisco. And Joel Horowitz, strategy and business development, IBM analyst. Guys, welcome back to theCUBE. Good to see you guys. >> Thank you for having us. >> It's great to be here, John. >> Give us the update on WANdisco. What's the relationship with IBM and WANdisco? 'Cause, you know. I can just almost see it, but I'm not going to predict. Just tell us. >> Okay, so, I think the last time we were on theCUBE, I was sitting with Re-ti-co who works very closely with Joe. And we began to talk about how our partnership was evolving. And of course, we were negotiating an OEM deal back then, so we really couldn't talk about it very much. But this week, I'm delighted to say that we announced, I think it's called IBM Big Replicate? >> Joel: Big Replicate, yeah. We have a big everything and Replicate's the latest edition. >> So it's going really well. It's OEM'd into IBM's analytics, big data products, and cloud products. >> Yeah, I'm smiling and smirking because we've had so many conversations, David, on theCUBE with you on and following your business through the bumpy road or the wild seas of big data. And it's been a really interesting tossing and turning of the industry. I mean, Joel, we've talked about it too. The innovation around Hadoop and then the massive slowdown and realization that cloud is now on top of it. The consumerization of the enterprise created a little shift in the value proposition, and then a massive rush to build enterprise grade, right? And you guys had that enterprise grade piece of it. IBM, certainly you're enterprise grade. You have enterprise everywhere. But the ecosystem had to evolve really fast. What happened? Share with the audience this shift. >> So, it's classic product adoption lifecycle and the buying audience has changed over that time continuum. In the very early days when we first started talking more at these events, when we were talking about Hadoop, we all really cared about whether it was Pig and Hive. >> You once had a distribution. That's a throwback. Today's Thursday, we'll do that tomorrow. >> And the buying audience has changed, and consequently, the companies involved in the ecosystem have changed. So where we once used to really care about all of those different components, we don't really care about the machinations below the application layer anymore. Some people do, yes, but by and large, we don't. And that's why cloud for example is so successful because you press a button, and it's there. And that, I think, is where the market is going to very, very quickly. So, it makes perfect sense for a company like WANdisco who've got 20, 30, 40, 50 sales people to move to a company like IBM that have 4 or 5,000 people selling our analytics products. >> Yeah, and so this is an OEM deal. Let's just get that news on the table. So, you're an OEM. IBM's going to OEM their product and brand it IBM, Big Replication? >> Yeah, it's part of our Big Insights Portfolio. We've done a great job at growing this product line over the last few years, with last year talking about how we decoupled all the value-as from the core distribution. So I'm happy to say that we're both part of the ODPI. It's an ODPI-certified distribution. That is Hadoop that we offer today for free. But then we've been adding not just in terms of the data management capabilities, but the partnership here that we're announcing with WANdisco and how we branded it as Big Replicate is squarely aimed at the data management market today. But where we're headed, as David points out, is really much bigger, right? We're talking about support for not only distributed storage and data, but we're also talking about a hybrid offering that will get you to the cloud faster. So not only does Big Replicate work with HDFS, it also works with the Swift objects store, which as you know, kind of the underlying storage for our cloud offering. So what we're hoping to see from this great partnership is as you see around you, Hadoop is a great market. But there's a lot more here when you talk about managing data that you need to consider. And I think hybrid is becoming a lot larger of a story than simply distributing your processing and your storage. It's becoming a lot more about okay, how do you offset different regions? How do you think through that there are multiple, I think there's this idea that there's one Hadoop cluster in an enterprise. I think that's factually wrong. I think what we're observing is that there's actually people who are spinning up, you know, multiple Hadoop distributions at the line of business for maybe a campaign or for maybe doing fraud detection, or maybe doing log file, whatever. And managing all those clusters, and they'll have Cloud Arrow. They'll have Hortonworks. They'll have IBM. They'll have all of these different distributions that they're having to deal with. And what we're offering is sanity. It's like give me sanity for how I can actually replicate that data. >> I love the name Big Replicate, fantastic. Big Insights, Big Replicate. And so go to market, you guys are going to have bigger sales force. It's a nice pop for you guys. I mean, it's good deal. >> We were just talking before we came on air about sort of a deal flow coming through. It's coming through, this potential deal flow coming through, which has been off the charts. I mean, obviously when you turn on the tap, and then suddenly you enable thousands and thousands of sales people to start selling your products. I mean, IBM, are doing a great job. And I think IBM are in a unique position where they own both cloud and on-prem. There are very few companies that own both the on-prem-- >> They're going to need to have that connection for the companies that are going hybrid. So hybrid cloud becomes interesting right now. >> Well, actually, it's, there's a theory that says okay, so, and we were just discussing this, the value of data lies in analytics, not in the data itself. It lies in you've been able to pull out information from that data. Most CIOs-- >> If you can get the data. >> If you can get the data. Let's assume that you've got the data. So then it becomes a question of, >> That's a big assumption. Yes, it is. (laughs) I just had Nancy Handling on about metadata. No, that's an issue. People have data they store they can't do anything with it. >> Exactly. And that's part of the problem because what you actually have to have is CPU slash processing power for an unknown amount of data any one moment in time. Now, that sounds like an elastic use case, and you can't do elastic on-prem. You can only do elastic in cloud. That means that virtually every distribution will have to be a hybrid distribution. IBM realized this years ago and began to build this hybrid infrastructure. We're going to help them to move data, completely consistent data, between on-prem and cloud, so when you query things in the cloud, it's exactly the same results and the correct results you get. >> And also the stability too on that. There's so many potential, as we've discussed in the past, that sounds simple and logical. To do an enterprise grade is pretty complex. And so it just gives a nice, stable enterprise grade component. >> I mean, the volumes of data that we're talking about here are just off the charts. >> Give me a use case of a customer that you guys are working with, or has there been any go-to-market activity or an ideal scenario that you guys see as a use case for this partnership? >> We're already seeing a whole bunch of things come through. >> What's the number one pattern that bubbles up to the top? Use case-wise. >> As Joel pointed out, that he doesn't believe that any one company just has one version of Hadoop behind their firewall. They have multiple vendors. >> 100% agree with that. >> So how do you create one, single cluster from all of those? >> John: That's one problem you solved. >> That's of course a very large problem. Second problem that we're seeing in spades is I have to move data to cloud to run analytics applications against it. That's huge. That required completely guaranteed consistent data between on-prem and cloud. And I think those two use cases alone account for pretty much every single company. >> I think there's even a third here. I think the third is actually, I think frankly there's a lot of inefficiencies in managing just HDFS and how many times you have to actually copy data. If I looked across, I think the standard right now is having like three copies. And actually, working with Big Replicate and WANdisco, you can actually have more assurances and actually have to make less copies across the cluster and actually across multiple clusters. If you think about that, you have three copies of the data sitting in this cluster. Likely, an analysts have a dragged a bunch of the same data in other clusters, so that's another multiple of three. So there's amount of waste in terms of the same data living across your enterprise. That I think there's a huge cost-savings component to this as well. >> Does this involve anything with Project Atlas at all? You guys are working with, >> Not yet, no. >> That project? It's interesting. We're seeing a lot of opening up the data, but all they're doing is creating versions of it. And so then it becomes version control of the data. You see a master or a centralization of data? Actually, not centralize, pull all the data in one spot, but why replicate it? Do you see that going on? I guess I'm not following the trend here. I can't see the mega trend going on. >> It's cloud. >> What's the big trend? >> The big trend is I need an elastic infrastructure. I can't build an elastic infrastructure on-premise. It doesn't make economic sense to build massive redundancy maybe three or four times the infrastructure I need on premise when I'm only going to use it maybe 10, 20% of the time. So the mega trend is cloud provides me with a completely economic, elastic infrastructure. In order to take advantage of that, I have to be able to move data, transactional data, data that changes all the time, into that cloud infrastructure and query it. That's the mega trend. It's as simple as that. >> So moving data around at the right time? >> And that's transaction. Anybody can say okay, press pause. Move the data, press play. >> So if I understand this correctly, and just, sorry, I'm a little slow. End of the day today. So instead of staging the data, you're moving data via the analytics engines. Is that what you're getting at? >> You use data that's being transformed. >> I think you're accessing data differently. I think today with Hadoop, you're accessing it maybe through like Flume or through Oozy, where you're building all these data pipelines that you have to manage. And I think that's obnoxious. I think really what you want is to use something like Apache Spark. Obviously, we've made a large investment in that earlier, actually, last year. To me, what I think I'm seeing is people who have very specific use cases. So, they want to do analysis for a particular campaign, and so they may just pull a bunch of data into memory from across their data environment. And that may be on the cloud. It may be from a third-party. It may be from a transactional system. It may be from anywhere. And that may be done in Hadoop. It may not, frankly. >> Yeah, this is the great point, and again, one of the themes on the show is, this is a question that's kind of been talked about in the hallways. And I'd love to hear your thoughts on this. Is there are some people saying that there's really no traction for Hadoop in the cloud. And that customers are saying, you know, it's not about just Hadoop in the cloud. I'm going to put in S3 or object store. >> You're right. I think-- >> Yeah, I'm right as in what? >> Every single-- >> There's no traction for Hadoop in the cloud? >> I'll tell you what customers tell us. Customers look at what they actually need from storage, and they compare whatever it is, Hadoop or any on-premise proprietor storage array and then look at what S3 and Swift and so on offer to them. And if you do a side-by-side comparison, there isn't really a difference between those two things. So I would argue that it's a fact that functionally, storage in cloud gives you all the functionality that any customer would need. And therefore, the relevance of Hadoop in cloud probably isn't there. >> I would add to that. So it really depends on how you define Hadoop. If you define Hadoop by the storage layer, then I would say for sure. Like HDFS versus an objects store, that's going to be a difficult one to find some sort of benefit there. But if you look at Hadoop, like I was talking to my friend Blake from Netflix, and I was asking him so I hear you guys are kind of like replatforming on Spark now. And he was basically telling me, well, sort of. I mean, they've invested a lot in Pig and Hive. So if you think it now about Hadoop as this broader ecosystem which you brought up Atlas, we talk about Ranger and Knox and all the stuff that keeps coming out, there's a lot of people who are still invested in the peripheral ecosystem around Hadoop as that central point. My argument would be that I think there's still going to be a place for distributed computing kind of projects. And now whether those will continue to interface through Yarn via and then down to HDFS, or whether that'll be Yarn on say an objects store or something and those projects will persist on their own. To me that's kind of more of how I think about the larger discussion around Hadoop. I think people have made a lot of investments in terms of that ecosystem around Hadoop, and that's something that they're going to have to think through. >> Yeah. And Hadoop wasn't really designed for cloud. It was designed for commodity servers, deployment with ease and at low cost. It wasn't designed for cloud-based applications. Storage in cloud was designed for storage in cloud. Right, that's with S3. That's what Swift and so on were designed specifically to do, and they fulfill most of those functions. But Joel's right, there will be companies that continue to use-- >> What's my whole argument? My whole argument is that why would you want to use Hadoop in the cloud when you can just do that? >> Correct. >> There's object store out. There's plenty of great storage opportunities in the cloud. They're mostly shoe-horning Hadoop, and I think that's, anyway. >> There are two classes of customers. There were customers that were born in the cloud, and they're not going to suddenly say, oh you know what, we need to build our own server infrastructure behind our own firewall 'cause they were born in the cloud. >> I'm going to ask you guys this question. You can choose to answer or not. Joel may not want to answer it 'cause he's from IBM and gets his wrist slapped. This is a question I got on DM. Hadoop ecosystem consolidation question. People are mailing in the questions. Now, keep sending me your questions if you don't want your name on it. Hold on, Hadoop system ecosystem. When will this start to happen? What is holding back the M and A? >> So, that's a great question. First of all, consolidation happens when you sort of reach that tipping point or leveling off, that inflection point where the market levels off, and we've reached market saturation. So there's no more market to go after. And the big guys like IBM and so on come in-- >> Or there was never a market to begin with. (laughs) >> I don't think that's the case, but yes, I see the point. Now, what's stopping that from happening today, and you're a naughty boy by the way for asking this question, is a lot of these companies are still very well funded. So while they still have cash on the balance sheet, of course, it's very, very hard for that to take place. >> You picked up my next question. But that's a good point. The VCs held back in 2009 after the crash of 2008. Sequoia's memo, you know, the good times role, or RIP good times. They stopped funding companies. Companies are getting funded, continually getting funding. Joel. >> So I don't think you can look at this market as like an isolated market like there's the Hadoop market and then there's a Spark market. And then even there's like an AI or cognitive market. I actually think this is all the same market. Machine learning would not be possible if you didn't have Hadoop, right? I wouldn't say it. It wouldn't have a resurgence that it has had. Mahout was one of the first machine learning languages that caught fire from Ted Dunning and others. And that kind of brought it back to life. And then Spark, I mean if you talk to-- >> John: I wouldn't say it creates it. Incubated. >> Incubated, right. >> And created that Renaissance-like experience. >> Yeah, deep learning, Some of those machine learning algorithms require you to have a distributed kind of framework to work in. And so I would argue that it's less of a consolidation, but it's more of an evolution of people going okay, there's distributed computing. Do I need to do that on-premise in this Hadoop ecosystem, or can I do that in the cloud, or in a growing Spark ecosystem? But I would argue there's other things happening. >> I would agree with you. I love both areas. My snarky comment there was never a market to begin with, what I'm saying there is that the monetization of commanding the hill that everyone's fighting for was just one of many hills in a bigger field of hills. And so, you could be in a cul-de-sac of being your own champion of no paying customers. >> What you have-- >> John: Or a free open-source product. >> Unlike the dotcom era where most of those companies were in the public markets, and you could actually see proper valuations, most of the companies, the unicorns now, most are not public. So the valuations are really difficult to, and the valuation metrics are hard to come by. There are only few of those companies that are in the public market. >> The cash story's right on. I think to Joel' point, it's easy to pivot in a market that's big and growing. Just 'cause you're in the wrong corner of the market pivoting or vectoring into the value is easier now than it was 10 years ago. Because, one, if you have a unicorn situation, you have cash on the bank. So they have a good flush cash. Your runway's so far out, you can still do your thing. If you're a startup, you can get time to value pretty quickly with the cloud. So again, I still think it's very healthy. In my opinion, I kind of think you guys have good analysis on that point. >> I think we're going to see some really cool stuff happen working together, and especially from what I'm seeing from IBM, in the fact that in the IT crowd, there is a behavioral change that's happening that Hadoop opened the door to. That we're starting to see more and more It professionals walk through. In the sense that, Hadoop has opened the door to not thinking of data as a liability, but actually thinking about data differently as an asset. And I think this is where this market does have an opportunity to continue to grow as long as we don't get carried away with trying to solve all of the old problems that we solved for on-premise data management. Like if we do that, then we're just, then there will be a consolidation. >> Metadata is a huge issue. I think that's going to be a big deal. And on the M and A, my feeling on the M and A is that, you got to buy something of value, so you either have revenue, which means customers, and or initial property. So, in a market of open source, it comes back down to the valuation question. If you're IBM or Oracle or HP, they can pivot too. And they can be agile. Now slower agile, but you know, they can literally throw some engineers at it. So if there's no customers in I and P, they can replicate, >> Exactly. >> That product. >> And we're seeing IBM do that. >> They don't know what they're buying. My whole point is if there's nothing to buy. >> I think it depends on, ultimately it depends on where we see people deriving value, and clearly in WANdisco, there's a huge amount of value that we're seeing our customers derive. So I think it comes down to that, and there is a lot of IP there, and there's a lot of IP in a lot of these companies. I think it's just a matter of widening their view, and I think WANdisco is probably the earliest to do this frankly. Was to recognize that for them to succeed, it couldn't just be about Hadoop. It actually had to expand to talk about cloud and talk about other data environments, right? >> Well, congratulations on the OEM deal. IBM, great name, Big Replicate. Love it, fantastic name. >> We're excited. >> It's a great product, and we've been following you guys for a long time, David. Great product, great energy. So I'm sure there's going to be a lot more deals coming on your. Good strategy is OEM strategy thing, huh? >> Oh yeah. >> It reduces sales cost. >> Gives us tremendous operational leverage. Getting 4,000, 5,000-- >> You get a great partner in IBM. They know the enterprise, great stuff. This is theCUBE bringing all the action here at Hadoop. IBM OEM deal with WANdisco all happening right here on theCUBE. Be back with more live coverage after this short break.

>>Ash it's, you know, what will that mean to my investment? And the announcement fusion IO 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 map R than, than other distribution. >>Jack, how 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 in the queue. I think, I think that either Dave or myself coined the term, the big three of big data, you guys ROMs cloud Cloudera map R and Hortonworks, really at the, at the beginning of the key players early on and Charles from Cloudera was just recently on. And, and he's like, oh no, this, this enterprise grade stuff has been kicked around. It's been there from the beginning. You guys have been there from the beginning and Matt BARR has never, ever waffled on your, on your messaging. You've always been very clear. Hey, we're going to take a dupe open source a dupe and turn it into an enterprise grade product. Right. So that's clear, right? That's, that's, that's a great, 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, like 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 haven't been any 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, than a marketing claim. And, you know, by enterprise grade, what we're talking about are some of the capabilities and features that they've grown to expect in their, their other enterprise applications. So, you know, the ability to meet full S SLA is full ha recovery from multiple failures, rolling upgrades, data protection was consistent snapshots business continuity with mirroring the ability to share a cluster across multiple groups and have, you know, volumes. I mean, there's a, 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, to ha it's more like moving to low availability. And, and there's just a lot of differences in terms of when we say enterprise grade with those features mean versus w what we view as kind of an incomplete story. So >>What do you, what do you mean by low availability? Well, I mean, it's tongue in cheek. It's nice. It's a good term. It's really saying, you know, just available when you sometimes is that what you mean? Is this not true availability? I mean, availability is 99.9%. Right? >>Right. So if you've got a, an ha solution that can't recover from multiple failures, that's downtime. If you've got an HBase 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, for a cluster. So, you know, there's a lot of details underneath that, but what it, 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 our folks addressing, what are the comp what's your competition doing in those areas of recoverability 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 energy. How would you give them the grade, the letter grade, if you will, candidly, compared to what you guys offer? Well, the, >>The first of all, it's take recoverability. You know, one of the tenants is 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, there's no point in time recovery for, for HDFS, for the files. And there's no point in time recovery for HBase tables. So there's snapshot support. It'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 a proprietary that, that whole debate we've, we've, we've heard about that. We talked about that before here on the cube. So just kind of reiterate for us your take. I mean, we, we hear perhaps because of the show we're at, there's a lot of talk about the open source nature of Hadoop and some of the purists, as you might call them are saying, it's gotta be open a hundred percent Patrick 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 make, to really spur adoption of a dupe and make it >>W w we're we're a part of the community we're, we've got, you know, commitment going on. We've, you know, pioneered and pushed a patchy drill, but we have done innovations as well. And I think that those innovations are really required to support and extend the, the whole ecosystem. So canonical distributes RN, three D distribution. We've got, you know, all our, our packages are, are available on get hub and, and open source. So it's not, it's not a binary debate. And I think the, the point being that there's companies that have jumped ahead and now that Peloton is, is, you know, pedaling faster and, and we'll, we'll catch up. We'll streamline. I think the difference is we rearchitected. So we're basically in a race car and, you know, are, are racing ahead with, with enterprise grade features that are required. And there's a lot of work that still needs to be done, needs to be accomplished before that full rearchitecture is, is in place. >>Well, I mean, I think for me, the proof is really in the pudding when you, 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 and 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 geological information, where do you guys do >>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, and, you know, ability for, for analysis. And then they've rolled out new features and new applications. One of which is to ship a kit out, have people spit in a tube, returned back and they do DNA matching and reveal additional details. So really some really fabulous leading edge things that are being done with, with the use of, of Hadoop. >>Interesting. So talk about when you went to, to work with them, what were some of their key requirements? Was it around, it was more around the enterprise enterprise, grade security and uptime kind of equation, or was it more around some of the analytics? What, what, what's the kind of the killer use case for them? >>It's kind of, you know, it's, it's hard with a specific company or even, you know, to generalize across companies. Cause they're really three main areas in terms of ease of use and administration dependability, which includes the full ha and then, and then performance. And in some cases, it's, it's just one of those that kind of drives it. And it's 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, the mounting and access and updates and using it in dynamic read-write. So what that means from an application level, it's, it's faster, it's much easier to administer and it's much easier and reliable for developers to, to utilize. >>I got to ask you about the marketing question cause I see, you know, map our, you guys have done a good job of marketing. Certainly we want to be thankful to you guys is supporting the cube in the past and you guys have been great supporters of our mission, but now the ecosystem's evolving a lot more competition. Claudia mentioned those eight companies they're tracking in quote Hadoop, and certainly Jeff and I, and, and SiliconANGLE by look at there's a lot more because Hadoop washing has been going on now for the term Hadoop watching me and jumping in and doing Hadoop, slapping that onto an existing solution. It's not been happening full, full, 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 more of the same and anything new even share. >>Yeah, I, I, I think as far as breaking above the noise, it will be our customers, their success and their use cases that really put the spotlight on what the differences are in terms of, of, you know, using a big data platform. And I think what, what companies will start to realize is I'd rather analogy between supply chain and the big, 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, 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 it can have the broadest set of functionality. So, you know, what we're delivering is a mission grade, you know, 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, you know, enterprise grade, no SQL applications. So now you can do, you know, high-speed consistent performance, real time operations in addition to batch streaming, integrated search, et cetera. So it's, it's really exciting to provide that platform and have organizations transform what they're doing. >>How's the feedback on with Ted Dunning? I haven't seen a lot of buzz on the Twittersphere is getting positive feedback here. He's a, 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, what's his role within Mapa and he's obviously playing in the open-source community. What's he up to these days, >>Chief application architect, he's on the leading edge of my house. So machine learning, so, you know, 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 that, 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 >>Chance to meet him in New York city had last had duke world at a, at a, a party and great guy, fantastic geek, and certainly is doing a great work and shout out to Ted. Congratulations, continue up that support. How's everyone else doing? How's John and Treevis doing how's the team at map are we're pedaling as best as you can growing >>Really quickly. No, we're just shifting gears. Would it be on pedaling >>Engine? >>Yeah. Give us an update on the company in terms of how the growth and kind of where you guys are moving that. >>Yeah. We're, we're expanding worldwide, you know, just this, you know, last few months we've opened up offices and in London and Munich and Paris, we're expanding in Asia, Japan and Korea. So w our, our sales and services and engineering, and basically across the whole company continues to expand rapidly. Some really great, interesting partnerships and, and a lot of growth Natalie's we add customers, but it's, it's nice to see customers that continue to really grow their use of map are within their organization, both in terms of amount of data that they're analyzing and the number of applications that they're bringing to bear on the platform. >>Well, that a little bit, because I think, you know, one of the, one of the trends we do see is when a company brings in big data, big data platform, and they might start experiment experimenting with it, build an application. And then maybe in the, maybe in the marketing department, then the sales guys see it and they say, well, maybe we can do something with that. How 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, how do you kind of, >>That's been the beauty of that is if you have a platform that can support those new applications. So if you know, mission critical workloads are not an issue, if you support volumes so that you can logically separate makes it much easier, which we have. So one of our customers Zions bank, they brought in Matt BARR 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 system. >>Yeah. They come in and you're sitting on a goldmine, there are use cases. And you also mentioned kind of, as you're expanding internationally, what's your take on the international market for big data to do specifically is, is the U S kind of a 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, where the rest of the, >>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, we're seeing deployment of class financial services and telecom, and it's, it's fairly broad recruit technologies there. The largest provider of recruiting services, indeed.com is one of their subsidiaries they're doing a lot with, with Hadoop and map are specifically, so it's, it's, it's been, it's been expanding rapidly. Fantastic. >>I also, you know, when you think about Europe, what's going on with Google and some of the, the privacy concerns even here, or I should say, is there, are 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, other locales? >>Yeah. There's typically by vertical, there's different, different requirements, HIPAA and healthcare, and basal to, and financial services. And so all of those, and it, it, 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 Hindu. >>I'm now kind of turning back to the technology. You mentioned Apache drill. I'd love to get an update on kind of where, where that stands. You know, it's put, then put that into context for people. We hear a lot about the SQL and Hadoop question here, where does drill fit into that, into that equation? >>Well, the, the, you know, there's a lot of different approaches to provide SQL access. A lot of that is driven by how do you, how do you leverage some of the talent and organization that, you know, speak SQL? So there's developments with respect to hive, you know, 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 it's, let's do full ANSI SQL. So it's full 2003 ANSI, sequel, not a SQL like, and that'll support the greatest number of applications and, you know, avoid a lot of support and, and issues. And the second design center is let's support a broad set of data sources. So nested sources like Jason scheme on discovery, and basically fitting it into an enterprise environment, which sometimes is kinda messy and can get messy as acquisitions happen, et cetera. So it's complimentary, it's about, you know, enabling interactive, low latency queries. >>Jack, I want to give you the final word. We are out of time. Thanks for coming on the cube. Really preached. Great to see you again, keep alumni, but final word. And we'll end the segment here on the cube 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 soundbite on Hadoop. >>I think I'll go back to how we started. It's not, if you used to do putz, how you use to do and, you know, 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. They were secure. We got a more coverage coming, Jack Norris with map R I'll say one of the big three original, big three, still on the, on the list in our mind, and the market's mind with a unique approach to Hadoop and the mid-June great. This is the cube I'm Jennifer with Jeff Kelly. We'll be right back after this short break, >>Let's settle the PR program out there and fighting gap tech news right there. Plenty of the attack was that providing a new gadget. Let's talk about the latest game name, but just the.

>>Okay. We're back here, live in New York city for big data week. This is siliconangle.tvs, exclusive coverage of Hadoop world strata plus Hadoop world big event, a big data week. And we just wrote a blog post on siliconangle.com calling this the south by Southwest for data geeks and, and, um, it's my prediction that this is going to turn into a, quite the geek Fest. Uh, obviously the crowd here is enormous packed and an amazing event. And, uh, we're excited. This is siliconangle.com. I'm the founder John ferry. I'm joined by cohost update >>Volante of Wiki bond.org, where people go for free research and peers collaborate to solve problems. And we're here with Jack Norris. Who's the vice president of market marketing at map are a company that we've been tracking for quite some time. Jack, welcome back to the cube. Thank you, Dave. I'm going to hand it to you. You know, we met quite a while ago now. It was well over a year ago and we were pushing at you guys and saying, well, you know, open source and nice look, we're solving problems for customers. We got the right model. We think, you know, this is, this is our strategy. We're sticking to it. Watch what happens. And like I said, I have to hand it to you. You guys are really have some great traction in the market and you're doing what you said. And so congratulations on that. I know you've got a lot more work to do, but >>Yeah, and actually the, the topic of openness is when it's, it's pretty interesting. Um, and, uh, you know, if you look at the different options out there, all of them are combining open source with some proprietary. Uh, now in the case of some distributions, it's very small, like an ODBC driver with a proprietary, um, driver. Um, but I think it represents that that any solution combining to make it more open is, is important. So what we've done is make innovations, but what we've made those innovations we've opened up and provided API. It's like NFS for standard access, like rest, like, uh, ODBC drivers, et cetera. >>So, so it's a spectrum. I mean, actually we were at Oracle open world a few weeks ago and you listen to Larry Ellison, talk about the Oracle public cloud mix of actually a very strong case that it's open. You can move data, it's all Java. So it's all about standards. Yeah. And, uh, yeah, it from an opposite, but it was really all about the business value. That's, that's what the bottom line is. So, uh, we had your CEO, John Schroeder on yesterday. Uh, John and I both were very impressed with, um, essentially what he described as your philosophy of we, we not as a product when we have, we have customers when we announce that product and, um, you know, that's impressive, >>Is that what he was also given some good feedback that startup entrepreneurs out there who are obviously a lot of action going on with the startup community. And he's basically said the same thing, get customers. Yeah. And that's it, that's all and use your tech, but don't be so locked into the tech, get the cutters, understand the needs and then deliver that. So you guys have done great. And, uh, I want to talk about the, the show here. Okay. Because, uh, you guys are, um, have a big booth and big presence here at the show. What, what did you guys are learning? I'll say how's the positioning, how's the new news hitting. Give us a quick update. So, >>Uh, a lot of news, uh, first started, uh, on Tuesday where we announced the M seven edition. And, uh, yeah, I brought a demo here for me, uh, for you all. Uh, because the, the big thing about M seven is what we don't have. So, uh, w we're not demoing Regents servers, we're not demoing compactions, uh, we're not demoing a lot of, uh, manual administration, uh, administrative tasks. So what that really means is that we took this stack. And if you look at HBase HBase today has about half of dupe users, uh, adopting HBase. So it's a lot of momentum in the market, uh, and, you know, use for everything from real-time analytics to kind of lightweight LTP processing. But it's an infrastructure that sits on top of a JVM that stores it's data in the Hadoop distributed file system that sits on a JVM that stores its data in a Linux file system that writes to disk. >>And so a lot of the complexity is that stack. And so as an administrator, you have to worry about how data gets permit, uh, uh, you know, kind of basically written across that. And you've got region servers to keep up, uh, when you're doing kind of rights, you have things called compactions, which increased response time. So it's, uh, it's a complex environment and we've spent quite a bit of time in, in collapsing that infrastructure and with the M seven edition, you've got files and tables together in the same layer writing directly to disc. So there's no region servers, uh, there's no compactions to deal with. There's no pre splitting of tables and trying to do manual merges. It just makes it much, much simpler. >>Let's talk about some of your customers in terms of, um, the profile of these guys are, uh, I'm assuming and correct me if I'm wrong, that you're not selling to the tire kickers. You're selling to the guys who actually have some experience with, with a dupe and have run into some of the limitations and you come in and say, Hey, we can solve some of those problems. Is that, is that, is that right? Can you talk about that a little bit >>Characterization? I think part of it is when you're in the evaluation process and when you first hear about Hadoop, it's kind of like the Gartner hype curve, right. And, uh, you know, this stuff, it does everything. And of course you got data protection, cause you've got things replicated across the cluster. And, uh, of course you've got scalability because you can just add nodes and so forth. Well, once you start using it, you realize that yes, I've got data replicated across the cluster, but if I accidentally delete something or if I've got some corruption that's replicated across the cluster too. So things like snapshots are really important. So you can return to, you know, what was it, five minutes before, uh, you know, performance where you can get the most out of your hardware, um, you know, ease of administration where I can cut this up into, into logical volumes and, and have policies at that whole level instead of at an individual file. >>So there's a, there's a bunch of features that really resonate with users after they've had some experience. And those tend to be our, um, you know, our, our kind of key customers. There's a, there's another phase two, which is when you're testing Hadoop, you're looking at, what's possible with this platform. What, what type of analytics can I do when you go into production? Now, all of a sudden you're looking at how does this fit in with my SLS? How does this fit in with my data protection, uh, policies, you know, how do I integrate with my different data sources? And can I leverage existing code? You know, we had one customer, um, you know, a large kind of a systems integrator for the federal government. They have a million lines of code that they were told to rewrite, to run with other distributions that they could use just out of the box with Matt BARR. >>So, um, let's talk about some of those customers. Can you name some names and get >>Sure. So, um, actually I'll, I'll, I'll talk with, uh, we had a keynote today and, uh, we had this beautiful customer video. They've had to cut because of times it's running in our booth and it's screaming on our website. And I think we've got to, uh, actually some of the bumper here, we kind of inserted. So, um, but I want to shout out to those because they ended up in the cutting room floor running it here. Yeah. So one was Rubicon project and, um, they're, they're an interesting company. They're a real-time advertising platform at auction network. They recently passed a Google in terms of number one ad reach as mentioned by comScore, uh, and a lot of press on that. Um, I particularly liked the headline that mentioned those three companies because it was measured by comScore and comScore's customer to map our customer. And Google's a key partner. >>And, uh, yesterday we announced a world record for the Hadoop pterosaur running on, running on Google. So, um, M seven for Rubicon, it allows them to address and replace different point solutions that were running alongside of Hadoop. And, uh, you know, it simplifies their, their potentially simplifies their architecture because now they have more things done with a single platform, increases performance, simplifies administration. Um, another customer is ancestry.com who, uh, you know, maybe you've seen their ads or heard, uh, some of their radio shots. Um, they're they do a tremendous amount of, of data processing to help family services and genealogy and figure out, you know, family backgrounds. One of the things they do is, is DNA testing. Uh, so for an internet service to do that, advanced technology is pretty impressive. And, uh, you know, you send them it's $99, I believe, and they'll send you a DNA kit spit in the tube, you send it back and then they process that and match and give you insights into your family background. So for them simplifying HBase meant additional performance, so they could do matches faster and really simplified administration. Uh, so, you know, and, and Melinda Graham's words, uh, you know, it's simpler because they're just not there. Those, those components >>Jack, I want to ask you about enterprise grade had duped because, um, um, and then, uh, Ted Dunning, because he was, he was mentioned by Tim SDS on his keynote speech. So, so you have some rockstars stars in the company. I was in his management team. We had your CEO when we've interviewed MC Sri vis and Google IO, and we were on a panel together. So as to know your team solid team, uh, so let's talk about, uh, Ted in a minute, but I want to ask you about the enterprise grade Hadoop conversation. What does that mean now? I mean, obviously you guys were very successful at first. Again, we were skeptics at first, but now your traction and your performance has proven this is a market for that kind of platform. What does that mean now in this, uh, at this event today, as this is evolving as Hadoop ecosystem is not just Hadoop anymore. It's other things. Yeah, >>There's, there's, there's three dimensions to enterprise grade. Um, the first is, is ease of use and ease of use from an administrator standpoint, how easy does it integrate into an existing environment? How easy does it, does it fit into my, my it policies? You know, do you run in a lights out data center? Does the Hadoop distribution fit into that? So that's, that's one whole dimension. Um, a key to that is, is, you know, complete NFS support. So it functions like, uh, you know, like standard storage. Uh, a second dimension is undependability reliability. So it's not just, you know, do you have a checkbox ha feature it's do you have automated stateful fail over? Do you have self healing? Can you handle multiple, uh, failures and, and, you know, automated recovery. So, you know, in a lights out data center, can you actually go there once a week? Uh, and then just, you know, replace drives. And a great example of that is one of our customers had a test cluster with, with Matt BARR. It was a POC went on and did other things. They had a power field, they came back a week later and the cluster was up and running and they hadn't done any manual tasks there. And they were, they were just blown away to the recovery process for the other distributions, a long laundry list of, >>So I've got to ask you, I got to ask you this, the third >>One, what's the third one, third one is performance and performance is, is, you know, kind of Ross' speed. It's also, how do you leverage the infrastructure? Can you take advantage of, of the network infrastructure, multiple Knicks? Can you take advantage of heterogeneous hardware? Can you mix and match for different workloads? And it's really about sharing a cluster for different use cases and, and different users. And there's a lot of features there. It's not just raw >>The existing it infrastructure policies that whole, the whole, what happens when something goes wrong. Can you automate that? And then, >>And it's easy to be dependable, fast, and speed the same thing, making HBase, uh, easy, dependable, fast with themselves. >>So the talk of the show right now, he had the keynote this morning is that map. Our marketing has dropped the big data term and going with data Kozum. Is that true? Is that true? So, Joe, Hellerstein just had a tweet, Joe, um, famous, uh, Cal Berkeley professor, computer science professor now is CEO of a startup. Um, what's the industry trifecta they're doing, and he had a good couple of epic tweets this week. So shout out to Joe Hellerstein, but Joel Hellison's tweet that says map our marketing has decided to drop the term big data and go with data Kozum with a shout out to George Gilder. So I'm kind of like middle intellectual kind of humor. So w w w what's what's your response to that? Is it true? What's happening? What is your, the embargo, the VP of marketing? >>Well, if you look at the big data term, I think, you know, there's a lot of big data washing going on where, um, you know, architectures that have been out there for 30 years or, you know, all about big data. Uh, so I think there's a, uh, there's the need for a more descriptive term. Um, the, the purpose of data Kozum was not to try to coin something or try to, you know, change a big data label. It was just to get people to take a step back and think, and to realize that we are in a massive paradigm shift. And, you know, with a shout out to George Gilder, acknowledging, you know, he recognized what the impact of, of making available compute, uh, meant he recognized with Telekom what bandwidth would mean. And if you look at the combination of we've got all this, this, uh, compute efficiency and bandwidth, now data them is, is basically taking those resources and unleashing it and changing the way we do things. >>And, um, I think, I think one of the ways to look at that is the new things that will be possible. And there's been a lot of focus on, you know, SQL interfaces on top of, of Hadoop, which are important. But I think some of the more interesting use cases are taking this machine J generated data that's being produced very, very rapidly and having automated operational analytics that can respond in a very fast time to change how you do business, either, how you're communicating with customers, um, how you're responding to two different, uh, uh, risk factors in the environment for fraud, et cetera, or, uh, just increasing and improving, um, uh, your response time to kind of cost events. We met earlier called >>Actionable insight. Then he said, assigning intent, you be able to respond. It's interesting that you talk about that George Gilder, cause we like to kind of riff and get into the concept abstract concepts, but he also was very big in supply side economics. And so if you look at the business value conversation, one of things we pointed out, uh, yesterday and this morning, so opening, um, review was, you know, the, the top conversations, insight and analytics, you know, as a killer app right now, the app market has not developed. And that's why we like companies like continuity and what you guys are doing under the hood is being worked on right at many levels, performance units of those three things, but analytics is a no brainer insight, but the other one's business value. So when you look at that kind of data, Kozum, I can see where you're going with that. >>Um, and that's kind of what people want, because it's not so much like I'm Republican because he's Republican George Gilder and he bought American spectator. Everyone knows that. So, so obviously he's a Republican, but politics aside, the business side of what big data is implementing is massive. Now that I guess that's a Republican concept. Um, but not really. I mean, businesses is, is, uh, all parties. So relative to data caused them. I mean, no one talks about e-business anymore. We talking to IBM at the IBM conference and they were saying, Hey, that was a great marketing campaign, but no one says, Hey, uh, you and eat business today. So we think that big data is going to have the same effect, which is, Hey, are you, do you have big data? No, it's just assumed. Yeah. So that's what you're basically trying to establish that it's not just about big. >>Yeah. Let me give you one small example, um, from a business value standpoint and, uh, Ted Dunning, you mentioned Ted earlier, chief application architect, um, and one of the coauthors of, of, uh, the book hoot, which deals with machine learning, uh, he dealt with one of our large financial services, uh, companies, and, uh, you know, one of the techniques on Hadoop is, is clustering, uh, you know, K nearest neighbors, uh, you know, different algorithms. And they looked at a particular process and they sped up that process by 30,000 times. So there's a blog post, uh, that's on our website. You can find out additional information on that. And I, >>There's one >>Point on this one point, but I think, you know, to your point about business value and you know, what does data Kozum really mean? That's an incredible speed up, uh, in terms of, of performance and it changes how companies can react in real time. It changes how they can do pattern recognition. And Google did a really interesting paper called the unreasonable effectiveness of data. And in there they say simple algorithms on big data, on massive amounts of data, beat a complex model every time. And so I think what we'll see is a movement away from data sampling and trying to do an 80 20 to looking at all your data and identifying where are the exceptions that we want to increase because there, you know, revenue exceptions or that we want to address because it's a cost or a fraud. >>Well, that's what I, I would give a shout out to, uh, to the guys that digital reasoning Tim asked he's plugged, uh, Ted. It was idolized him in terms of his work. Obviously his work is awesome, but two, he brought up this concept of understanding gap and he showed an interesting chart in his keynote, which was the date explosion, you know, it's up and, you know, straight up, right. It's massive amount of data, 64% unstructured by his calculation. Then he showed out a flat line called attention. So as data's been exploding over time, going up attention mean user attention is flat with some uptick maybe, but so users and humans, they can't expand their mind fast enough. So machine learning technologies have to bridge that gap. That's analytics, that's insight. >>Yeah. There's a big conversation now going on about more data, better models, people trying to squint through some of the comments that Google made and say, all right, does that mean we just throw out >>The models and data trumps algorithms, data >>Trumps algorithms, but the question I have is do you think, and your customer is talking about, okay, well now they have more data. Can I actually develop better algorithms that are simpler? And is it a virtuous cycle? >>Yeah, it's I, I think, I mean, uh, there are there's, there are a lot of debate here, a lot of information, but I think one of the, one of the interesting things is given that compute cycles, given the, you know, kind of that compute efficiency that we have and given the bandwidth, you can take a model and then iterate very quickly on it and kind of arrive at, at insight. And in the past, it was just that amount of data in that amount of time to process. Okay. That could take you 40 days to get to the point where you can do now in hours. Right. >>Right. So, I mean, the great example is fraud detection, right? So we used the sample six months later, Hey, your credit card might've been hacked. And now it's, you know, you got a phone call, you know, or you can't use your credit card or whatever it is. And so, uh, but there's still a lot of use cases where, you know, whether is an example where modeling and better modeling would be very helpful. Uh, excellent. So, um, so Dana custom, are you planning other marketing initiatives around that? Or is this sort of tongue in cheek fun? Throw it out there. A little red meat into the chum in the waters is, >>You know, what really motivated us was, um, you know, the cubes here talking, you know, for the whole day, what could we possibly do to help give them a topic of conversation? >>Okay. Data cosmos. Now of course, we found that on our proprietary HBase tools, Jack Norris, thanks for coming in. We appreciate your support. You guys have been great. We've been following you and continue to follow. You've been a great support of the cube. Want to thank you personally, while we're here. Uh, Matt BARR has been generous underwriter supportive of our great independent editorial. We want to recognize you guys, thanks for your support. And we continue to look forward to watching you guys grow and kick ass. So thanks for all your support. And we'll be right back with our next guest after this short break. >>Thank you. >>10 years ago, the video news business believed the internet was a fat. The science is settled. We all know the internet is here to stay bubbles and busts come and go. But the industry deserves a news team that goes the distance coming up on social angle are some interesting new metrics for measuring the worth of a customer on the web. 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