>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have with us Eric Herzog. He is the Chief Marketing Officer and VP of Global Channels at the IBM Storage Division. Thanks so much for coming on theCUBE once again, Eric. >> Well, thank you. We always love to be on theCUBE and talk to all of theCUBE analysts about various topics, data, storage, multi-cloud, all the works. >> And before the cameras were rolling, we were talking about how you might be the biggest CUBE alum in the sense of you've been on theCUBE more times than anyone else. >> I know I'm in the top five, but I may be number one, I have to check with Dave Vellante and crew and see. >> Exactly and often wearing a Hawaiian shirt. >> Yes. >> Yes, I was on theCUBE last week from CISCO Live. I was not wearing a Hawaiian shirt. And Stu and John gave me a hard time about why was not I wearing a Hawaiian shirt? So I make sure I showed up to the DataWorks show- >> Stu, Dave, get a load. >> You're in California with a tan, so it fits, it's good. >> So we were talking a little bit before the cameras were rolling and you were saying one of the points that is sort of central to your professional life is it's not just about the storage, it's about the data. So riff on that a little bit. >> Sure, so at IBM we believe everything is data driven and in fact we would argue that data is more valuable than oil or diamonds or plutonium or platinum or silver to anything else. It is the most viable asset, whether you be a global Fortune 500, whether you be a midsize company or whether you be Herzogs Bar and Grill. So data is what you use with your suppliers, with your customers, with your partners. Literally everything around your company is really built around the data so most effectively managing it and make sure, A, it's always performant because when it's not performant they go away. As you probably know, Google did a survey that one, two, after one, two they go off your website, they click somewhere else so has to be performant. Obviously in today's 365, 7 by 24 company it needs to always be resilient and reliable and it always needs to be available, otherwise if the storage goes down, guess what? Your AI doesn't work, your Cloud doesn't work, whatever workload, if you're more traditional, your Oracle, Sequel, you know SAP, none of those workloads work if you don't have a solid storage foundation underneath your data driven enterprise. >> So with that ethos in mind, talk about the products that you are launching, that you newly launched and also your product roadmap going forward. >> Sure, so for us everything really is that storage is this critical foundation for the data driven, multi Cloud enterprise. And as I've said before on theCube, all of our storage software's now Cloud-ified so if you need to automatically tier out to IBM Cloud or Amazon or Azure, we automatically will move the data placement around from one premise out to a Cloud and for certain customers who may be multi Cloud, in this case using multiple private Cloud providers, which happens due to either legal reasons or procurement reasons or geographic reasons for the larger enterprises, we can handle that as well. That's part of it, second thing is we just announced earlier today an artificial intelligence, an AI reference architecture, that incorporates a full stack from the very bottom, both servers and storage, all the way up through the top layer, then the applications on top, so we just launched that today. >> AI for storage management or AI for run a range of applications? >> Regular AI, artificial intelligence from an application perspective. So we announced that reference architecture today. Basically think of the reference architecture as your recipe, your blueprint, of how to put it all together. Some of the components are from IBM, such as Spectrum Scale and Spectrum Computing from my division, our servers from our Cloud division. Some are opensource, Tensor, Caffe, things like that. Basic gives you what the stack needs to be, and what you need to do in various AI workloads, applications and use cases. >> I believe you have distributed deep learning as an IBM capability, that's part of that stack, is that correct? >> That is part of the stack, it's like in the middle of the stack. >> Is it, correct me if I'm wrong, that's containerization of AI functionality? >> Right. >> For distributed deployment? >> Right. >> In an orchestrated Kubernetes fabric, is that correct? >> Yeah, so when you look at it from an IBM perspective, while we clearly support the virtualized world, the VM wares, the hyper V's, the KVMs and the OVMs, and we will continue to do that, we're also heavily invested in the container environment. For example, one of our other divisions, the IBM Cloud Private division, has announced a solution that's all about private Clouds, you can either get it hosted at IBM or literally buy our stack- >> Rob Thomas in fact demoed it this morning, here. >> Right, exactly. And you could create- >> At DataWorks. >> Private Cloud initiative, and there are companies that, whether it be for security purposes or whether it be for legal reasons or other reasons, don't want to use public Cloud providers, be it IBM, Amazon, Azure, Google or any of the big public Cloud providers, they want a private Cloud and IBM either A, will host it or B, with IBM Cloud Private. All of that infrastructure is built around a containerized environment. We support the older world, the virtualized world, and the newer world, the container world. In fact, our storage, allows you to have persistent storage in a container's environment, Dockers and Kubernetes, and that works on all of our block storage and that's a freebie, by the way, we don't charge for that. >> You've worked in the data storage industry for a long time, can you talk a little bit about how the marketing message has changed and evolved since you first began in this industry and in terms of what customers want to hear and what assuages their fears? >> Sure, so nobody cares about speeds and feeds, okay? Except me, because I've been doing storage for 32 years. >> And him, he might care. (laughs) >> But when you look at it, the decision makers today, the CIOs, in 32 years, including seven start ups, IBM and EMC, I've never, ever, ever, met a CIO who used to be a storage guy, ever. So, they don't care. They know that they need storage and the other infrastructure, including servers and networking, but think about it, when the app is slow, who do they blame? Usually they blame the storage guy first, secondarily they blame the server guy, thirdly they blame the networking guy. They never look to see that their code stack is improperly done. Really what you have to do is talk applications, workloads and use cases which is what the AI reference architecture does. What my team does in non AI workloads, it's all about, again, data driven, multi Cloud infrastructure. They want to know how you're going to make a new workload fast AI. How you're going to make their Cloud resilient whether it's private or hybrid. In fact, IBM storage sells a ton of technology to large public Cloud providers that do not have the initials IBM. We sell gobs of storage to other public Cloud providers, both big, medium and small. It's really all about the applications, workloads and use cases, and that's what gets people excited. You basically need a position, just like I talked about with the AI foundations, storage is the critical foundation. We happen to be, knocking on wood, let's hope there's no earthquake, since I've lived here my whole life, and I've been in earthquakes, I was in the '89 quake. Literally fell down a bunch of stairs in the '89 quake. If there's an earthquake as great as IBM storage is, or any other storage or servers, it's crushed. Boom, you're done! Okay, well you need to make sure that your infrastructure, really your data, is covered by the right infrastructure and that it's always resilient, it's always performing and is always available. And that's what IBM drives is about, that's the message, not about how many gigabytes per second in bandwidth or what's the- Not that we can't spew that stuff when we talk to the right person but in general people don't care about it. What they want to know is, "Oh that SAP workload took 30 hours and now it takes 30 minutes?" We have public references that will say that. "Oh, you mean I can use eight to ten times less storage for the same money?" Yes, and we have public references that will say that. So that's what it's really about, so storage is really more from really a speeds and feeds Nuremberger sort of thing, and now all the Nurembergers are doing AI and Caffe and TensorFlow and all of that, they're all hackers, right? It used to be storage guys who used to do that and to a lesser extent server guys and definitely networking guys. That's all shifted to the software side so you got to talk the languages. What can we do with Hortonworks? By the way we were named in Q1 of 2018 as the Hortonworks infrastructure partner of the year. We work with Hortonworks all time, at all levels, whether it be with our channel partners, whether it be with our direct end users, however the customer wants to consume, we work with Hortonworks very closely and other providers as well in that big data analytics and the AI infrastructure world, that's what we do. >> So the containerizations side of the IBM AI stack, then the containerization capabilities in Hortonworks Data Platform 3.0, can you give us a sense for how you plan to, or do you plan at IBM, to work with Hortonworks to bring these capabilities, your reference architecture, into more, or bring their environment for that matter, into more of an alignment with what you're offering? >> So we haven't an exact decision of how we're going to do it, but we interface with Hortonworks on a continual basis. >> Yeah. >> We're working to figure out what's the right solution, whether that be an integrated solution of some type, whether that be something that we do through an adjunct to our reference architecture or some reference architecture that they have but we always make sure, again, we are their partner of the year for infrastructure named in Q1, and that's because we work very tightly with Hortonworks and make sure that what we do ties out with them, hits the right applications, workloads and use cases, the big data world, the analytic world and the AI world so that we're tied off, you know, together to make sure that we deliver the right solutions to the end user because that's what matters most is what gets the end users fired up, not what gets Hortonworks or IBM fired up, it's what gets the end users fired up. >> When you're trying to get into the head space of the CIO, and get your message out there, I mean what is it, what would you say is it that keeps them up at night? What are their biggest pain points and then how do you come in and solve them? >> I'd say the number one pain point for most CIOs is application delivery, okay? Whether that be to the line of business, put it this way, let's take an old workload, okay? Let's take that SAP example, that CIO was under pressure because they were trying, in this case it was a giant retailer who was shipping stuff every night, all over the world. Well guess what? The green undershirts in the wrong size, went to Paducah, Kentucky and then one of the other stores, in Singapore, which needed those green shirts, they ended up with shoes and the reason is, they couldn't run that SAP workload in a couple hours. Now they run it in 30 minutes. It used to take 30 hours. So since they're shipping every night, you're basically missing a cycle, essentially and you're not delivering the right thing from a retail infrastructure perspective to each of their nodes, if you will, to their retail locations. So they care about what do they need to do to deliver to the business the right applications, workloads and use cases on the right timeframe and they can't go down, people get fired for that at the CIO level, right? If something goes down, the CIO is gone and obviously for certain companies that are more in the modern mode, okay? People who are delivering stuff and their primary transactional vehicle is the internet, not retail, not through partners, not through people like IBM, but their primary transactional vehicle is a website, if that website is not resilient, performant and always reliable, then guess what? They are shut down and they're not selling anything to anybody, which is to true if you're Nordstroms, right? Someone can always go into the store and buy something, right, and figure it out? Almost all old retailers have not only a connection to core but they literally have a server and storage in every retail location so if the core goes down, guess what, they can transact. In the era of the internet, you don't do that anymore. Right? If you're shipping only on the internet, you're shipping on the internet so whether it be a new workload, okay? An old workload if you're doing the whole IOT thing. For example, I know a company that I was working with, it's a giant, private mining company. They have those giant, like three story dump trucks you see on the Discovery Channel. Those things cost them a hundred million dollars, so they have five thousand sensors on every dump truck. It's a fricking dump truck but guess what, they got five thousand sensors on there so they can monitor and make sure they take proactive action because if that goes down, whether these be diamond mines or these be Uranium mines or whatever it is, it costs them hundreds of millions of dollars to have a thing go down. That's, if you will, trying to take it out of the traditional, high tech area, which we all talk about, whether it be Apple or Google, or IBM, okay great, now let's put it to some other workload. In this case, this is the use of IOT, in a big data analytics environment with AI based infrastructure, to manage dump trucks. >> I think you're talking about what's called, "digital twins" in a networked environment for materials management, supply chain management and so forth. Are those requirements growing in terms of industrial IOT requirements of that sort and how does that effect the amount of data that needs to be stored, the sophistication of the AI and the stream competing that needs to be provisioned? Can you talk to that? >> The amount of data is growing exponentially. It's growing at yottabytes and zettabytes a year now, not at just exabytes anymore. In fact, everybody on their iPhone or their laptop, I've got a 10GB phone, okay? My laptop, which happens to be a Power Book, is two terabytes of flash, on a laptop. So just imagine how much data's being generated if you're doing in a giant factory, whether you be in the warehouse space, whether you be in healthcare, whether you be in government, whether you be in the financial sector and now all those additional regulations, such as GDPR in Europe and other regulations across the world about what you have to do with your healthcare data, what you have to do with your finance data, the amount of data being stored. And then on top of it, quite honestly, from an AI big data analytics perspective, the more data you have, the more valuable it is, the more you can mine it or the more oil, it's as if the world was just oil, forget the pollution side, let's assume oil didn't cause pollution. Okay, great, then guess what? You would be using oil everywhere and you wouldn't be using solar, you'd be using oil and by the way you need more and more and more, and how much oil you have and how you control that would be the power. That right now is the power of data and if anything it's getting more and more and more. So again, you always have to be able to be resilient with that data, you always have to interact with things, like we do with Hortonworks or other application workloads. Our AI reference architecture is another perfect example of the things you need to do to provide, you know, at the base infrastructure, the right foundation. If you have the wrong foundation to a building, it falls over. Whether it be your house, a hotel, this convention center, if it had the wrong foundation, it falls over. >> Actually to follow the oil analogy just a little bit further, the more of this data you have, the more PII there is and it usually, and the more the workloads need to scale up, especially for things like data masking. >> Right. >> When you have compliance requirements like GDPR, so you want to process the data but you need to mask it first, therefore you need clusters that conceivably are optimized for high volume, highly scalable masking in real time, to drive the downstream app, to feed the downstream applications and to feed the data scientist, you know, data lakes, whatever, and so forth and so on? >> That's why you need things like Incredible Compute which IBM offers with the Power Platform. And why you need storage that, again, can scale up. >> Yeah. >> Can get as big as you need it to be, for example in our reference architecture, we use both what we call Spectrum Scale, which is a big data analytics workload performance engine, it has multiple threaded, multi tasking. In fact one of the largest banks in the world, if you happen to bank with them, your credit card fraud is being done on our stuff, okay? But at the same time we have what's called IBM Cloud Object Storage which is an object store, you want to take every one of those searches for fraud and when they find out that no one stole my MasterCard or the Visa, you still want to put it in there because then you mine it later and see patterns of how people are trying to steal stuff because it's all being done digitally anyway. You want to be able to do that. So you A, want to handle it very quickly and resiliently but then you want to be able to mine it later, as you said, mining the data. >> Or do high value anomaly detection in the moment to be able to tag the more anomalous data that you can then sift through later or maybe in the moment for realtime litigation. >> Well that's highly compute intensive, it's AI intensive and it's highly storage intensive on a performance side and then what happens is you store it all for, lets say, further analysis so you can tell people, "When you get your Am Ex card, do this and they won't steal it." Well the only way to do that, is you use AI on this ocean of data, where you're analyzing all this fraud that has happened, to look at patterns and then you tell me, as a consumer, what to do. Whether it be in the financial business, in this case the credit card business, healthcare, government, manufacturing. One of our resellers actually developed an AI based tool that can scan boxes and cans for faults on an assembly line and actually have sold it to a beer company and to a soda company that instead of people looking at the cans, like you see on the Food Channel, to pull it off, guess what? It's all automatically done. There's no people pulling the can off, "Oh, that can is damaged" and they're looking at it and by the way, sometimes they slip through. Now, using cameras and this AI based infrastructure from IBM, with our storage underneath the hood, they're able to do this. >> Great. Well Eric thank you so much for coming on theCUBE. It's always been a lot of fun talking to you. >> Great, well thank you very much. We love being on theCUBE and appreciate it and hope everyone enjoys the DataWorks conference. >> We will have more from DataWorks just after this. (techno beat music)

>>The queue at Hadoop summit, 2014 is brought to you by anchor sponsor Hortonworks. We do, I do. And headline sponsor when disco we make Hadoop invincible >>Okay. Welcome back. Everyone live here in Silicon valley in San Jose. This is a dupe summit. This is Silicon angle and Wiki bonds. The cube is our flagship program. We go out to the events and extract the signal to noise. I'm John barrier, the founder SiliconANGLE joins my cohost, Jeff Kelly, top big data analyst in the, in the community. Our next guest, Jack Norris, COO of map R security enterprise. That's the buzz of the show and it was the buzz of OpenStack summit. Another open source show. And here this year, you're just seeing move after, move at the moon, talking about a couple of critical issues. Enterprise grade Hadoop, Hortonworks announced a big acquisition when all in, as they said, and now cloud era follows suit with their news. Today, I, you sitting back saying, they're catching up to you guys. I mean, how do you look at that? I mean, cause you guys have that's the security stuff nailed down. So what Dan, >>You feel about that now? I think I'm, if you look at the kind of Hadoop market, it's definitely moving from a test experimental phase into a production phase. We've got tremendous customers across verticals that are doing some really interesting production use cases. And we recognized very early on that to really meet the needs of customers required some architectural innovation. So combining the open source ecosystem packages with some innovations underneath to really deliver high availability, data protection, disaster recovery features, security is part of that. But if you can't predict the PR protect the data, if you can't have multitenancy and separate workflows across the cluster, then it doesn't matter how secure it is. You know, you need those. >>I got to ask you a direct question since we're here at Hadoop summit, because we get this question all the time. Silicon lucky bond is so successful, but I just don't understand your business model without plates were free content and they have some underwriters. So you guys have been very successful yet. People aren't looking at map are as good at the quiet leader, like you doing your business, you're making money. Jeff. He had some numbers with us that in the Hindu community, about 20% are paying subscriptions. That's unlike your business model. So explain to the folks out there, the business model and specifically the traction because you have >>Customers. Yeah. Oh no, we've got, we've got over 500 paying customers. We've got at least $1 million customer in seven different verticals. So we've got breadth and depth and our business model is simple. We're an enterprise software company. That's looking at how to provide the best of open source as well as innovations underneath >>The most open distribution of Hadoop. But you add that value separately to that, right? So you're, it's not so much that you're proprietary at all. Right. Okay. >>You clarify that. Right. So if you look at, at this exciting ecosystem, Hadoop is fairly early in its life cycle. If it's a commoditization phase like Linux or, or relational database with my SQL open source, kind of equates the whole technology here at the beginning of this life cycle, early stages of the life cycle. There's some architectural innovations that are really required. If you look at Hadoop, it's an append only file system relying on Linux. And that really limits the types of operations. That types of use cases that you can do. What map ours done is provide some deep architectural innovations, provide complete read-write file systems to integrate data protection with snapshots and mirroring, et cetera. So there's a whole host of capabilities that make it easy to integrate enterprise secure and, and scale much better. Do you think, >>I feel like you were maybe a little early to the market in the sense that we heard Merv Adrian and his keynote this morning. Talk about, you know, it's about 10 years when you start to get these questions about security and governance and we're about nine years into Hadoop. Do you feel like maybe you guys were a little early and now you're at a tipping point, whereas these more, as more and more deployments get ready to go to production, this is going to be an area that's going to become increasingly important. >>I think, I think our timing has been spectacular because we, we kind of came out at a time when there was some customers that were really serious about Hadoop. We were able to work closely with them and prove our technology. And now as the market is just ramping, we're here with all of those features that they need. And what's a, what's an issue. Is that an incremental improvement to provide those kind of key features is not really possible if the underlying architecture isn't there and it's hard to provide, you know, online real-time capabilities in a underlying platform that's append only. So the, the HDFS layer written in Java, relying on the Linux file system is kind of the, the weak underbelly, if you will, of, of the ecosystem. There's a lot of, a lot of important developments happening yarn on top of it, a lot of really kind of exciting things. So we're actively participating in including Apache drill and on top of a complete read-write file system and integrated Hindu database. It just makes it all come to life. >>Yeah. I mean, those things on top are critical, but you know, it's, it's the underlying infrastructure that, you know, we asked, we keep on community about that. And what's the, what are the things that are really holding you back from Paducah and production and the, and the biggest challenge is they cited worth high availability, backup, and recovery and maintaining performance at scale. Those are the top three and that's kind of where Matt BARR has been focused, you know, since day one. >>So if you look at a major retailer, 2000 nodes and map bar 50 unique applications running on a single cluster on 10,000 jobs a day running on top of that, if you look at the Rubicon project, they recently went public a hundred million add actions, a hundred billion ad auctions a day. And on top of that platform, beats music that just got acquired for $3 billion. Basically it's the underlying map, our engine that allowed them to scale and personalize that music service. So there's a, there's a lot of proof points in terms of how quickly we scale the enterprise grade features that we provide and kind of the blending of deep predictive analytics in a batch environment with online capabilities. >>So I got to ask you about your go to market. I'll see Cloudera and Hortonworks have different business models. Just talk about that, but Cloudera got the massive funding. So you get this question all the time. What do you, how do you counter that army and the arms race? I think >>I just wrote an article in Forbes and he says cash is not a strategy. And I think that was, that was an excellent, excellent article. And he goes in and, you know, in this fast growing market, you know, an amount of money isn't necessarily translate to architectural innovations or speeding the development of that. This is a fairly fragmented ecosystem in terms of the stack that runs on top of it. There's no single application or single vendor that kind of drives value. So an acquisition strategy is >>So your field Salesforce has direct or indirect, both mixable. How do you handle the, because Cloudera has got feet on the street and every squirrel will find it, not if they're parked there, parking sales reps and SCS and all the enterprise accounts, you know, they're going to get the, squirrel's going to find a nut once in awhile. Yeah. And they're going to actually try to engage the clients. So, you know, I guess it is a strategy if they're deploying sales and marketing, right? So >>The beauty about that, and in fact, we're all in this together in terms of sharing an API and driving an ecosystem, it's not a fragmented market. You can start with one distribution and move to another, without recompiling or without doing any sort of changes. So it's a fairly open community. If this were a vendor lock-in or, you know, then spending money on brand, et cetera, would, would be important. Our focus is on the, so the sales execution of direct sales, yes, we have direct sales. We also have partners and it depends on the geographies as to what that percentage is. >>And John Schroeder on with the HP at fifth big data NYC has updated the HP relationship. >>Oh, excellent. In fact, we just launched our application gallery app gallery, make it very easy for administrators and developers and analysts to get access and understand what's available in the ecosystem. That's available directly on our website. And one of the featured applications there today is an integration with the map, our sandbox and HP Vertica. So you can get early access, try it and get the best of kind of enterprise grade SQL first, >>First Hadoop app store, basically. Yeah. If you want to call it that way. Right. So like >>Sure. Available, we launched with close to 30, 30 with, you know, a whole wave kind of following that. >>So talk a little bit about, you know, speaking of verdict and kind of the sequel on Hadoop. So, you know, there's a lot of talk about that. Some confusion about the different methods for applying SQL on predicts or map art takes an open approach. I know you'll support things like Impala from, from a competitor Cloudera, talk about that approach from a map arts perspective. >>So I guess our, our, our perspective is kind of unbiased open source. We don't try to pick and choose and dictate what's the right open source based on either our participation or some community involvement. And the reality is with multiple applications being run on the platform, there are different use cases that make difference, you know, make different sense. So whether it's a hive solution or, you know, drill drills available, or HP Vertica people have the choice. And it's part of, of a broad range of capabilities that you want to be able to run on the platform for your workflows, whether it's SQL access or a MapReduce or a spark framework shark, et cetera. >>So, yeah, I mean there is because there's so many different there's spark there's, you know, you can run HP Vertica, you've got Impala, you've got hive. And the stinger initiative is, is that whole kind of SQL on Hadoop ecosystem, still working itself out. Are we going to have this many options in a year or two years from now? Or are they complimentary and potentially, you know, each has its has its role. >>I think the major differences is kind of how it deals with the new data formats. Can it deal with self-describing data? Sources can leverage, Jason file does require a centralized metadata, and those are some of the perspectives and advantages say the Apache drill has to expand the data sets that are possible enabled data exploration without dependency on a, on an it administrator to define that, that metadata. >>So another, maybe not always as exciting, but taking workloads from existing systems, moving them to Hadoop is one of the ways that a lot of people get started with, to do whether associated transformation workloads or there's something in that vein. So I know you've announced a partnership with Syncsort and that's one of the things that they focus on is really making it as easy as possible to meet those. We'll talk a little bit about that partnership, why that makes sense for you and, and >>When your customer, I think it's a great proof point because we announced that partnership around mainframe offload, we have flipped comScore and experience in that, in that press release. And if you look at a workload on a mainframe going to duke, that that seems like that's a, that's really an oxymoron, but by having the capabilities that map R has and making that a system of record with that full high availability and that data protection, we're actually an option to offload from mainframe offload, from sand processing and provide a really cost effective, scalable alternative. And we've got customers that had, had tried to offload from the mainframe multiple times in the past, on successfully and have done it successfully with Mapbox. >>So talk a little bit more about kind of the broader partnership strategy. I mean, we're, we're here at Hadoop summit. Of course, Hortonworks talks a lot about their partnerships and kind of their reseller arrangements. Fedor. I seem to take a little bit more of a direct approach what's map R's approach to kind of partnering and, and as that relates to kind of resell arrangements and things like, >>I think the app gallery is probably a great proof point there. The strategy is, is an ecosystem approach. It's having a collection of tools and applications and management facilities as well as applications on top. So it's a very open strategy. We focus on making sure that we have open API APIs at that application layer, that it's very easy to get data in and out. And part of that architecture by presenting standard file system format, by allowing non Java applications to run directly on our platform to support standard database connections, ODBC, and JDBC, to provide database functionality. In addition to kind of this deep predictive analytics really it's about supporting the broadest set of applications on top of a single platform. What we're seeing in this kind of this, this modern architecture is data gravity matters. And the more processing you can do on a single platform, the better off you are, the more agile, the more competitive, right? >>So in terms of, so you're partnering with people like SAS, for example, to kind of bring some of the, some of the analytic capabilities into the platform. Can you kind of tell us a little bit about any >>Companies like SAS and revolution analytics and Skytree, and I mean, just a whole host of, of companies on the analytics side, as well as on the tools and visualization, et cetera. Yeah. >>Well, I mean, I, I bring up SAS because I think they, they get the fact that the, the whole data gravity situation is they've got it. They've got to go to where the data is and not have the data come to them. So, you know, I give them credit for kind of acknowledging that, that kind of big data truth ism, that it's >>All going to the data, not bringing the data >>To the computer. Jack talk about the success you had with the customers had some pretty impressive numbers talking about 500 customers, Merv agent. The garden was on with us earlier, essentially reiterating not mentioning that bar. He was just saying what you guys are doing is right where the puck is going. And some think the puck is not even there at the same rink, some other vendors. So I gotta give you props on that. So what I want you to talk about the success you have in specifically around where you're winning and where you're successful, you guys have struggled with, >>I need to improve on, yeah, there's a, there's a whole class of applications that I think Hadoop is enabling, which is about operations in analytics. It's taking this, this higher arrival rate machine generated data and doing analytics as it happens and then impacting the business. So whether it's fraud detection or recommendation engines, or, you know, supply chain applications using sensor data, it's happening very, very quickly. So a system that can tolerate and accept streaming data sources, it has real-time operations. That is 24 by seven and highly available is, is what really moves the needle. And that's the examples I used with, you know, add a Rubicon project and, you know, cable TV, >>The very outcome. What's the primary outcomes your clients want with your product? Is it stability? And the platform has enabled development. Is there a specific, is there an outcome that's consistent across all your wins? >>Well, the big picture, some of them are focused on revenues. Like how do we optimize revenue either? It's a new data source or it's a new application or it's existing application. We're exploding the dataset. Some of it's reducing costs. So they want to do things like a mainframe offload or data warehouse offload. And then there's some that are focused on risk mitigation. And if there's anything that they have in common it's, as they moved from kind of test and looked at production, it's the key capabilities that they have in enterprise systems today that they want to make sure they're in Hindu. So it's not, it's not anything new. It's just like, Hey, we've got SLS and I've got data protection policies, and I've got a disaster recovery procedure. And why can't I expect the same level of capabilities in Hindu that I have today in those other systems. >>It's a final question. Where are you guys heading this year? What's your key objectives. Obviously, you're getting these announcements as flurry of announcements, good success state of the company. How many employees were you guys at? Give us a quick update on the numbers. >>So, you know, we just reported this incredible momentum where we've tripled core growth year over year, we've added a tremendous amount of customers. We're over 500 now. So we're basically sticking to our knitting, focusing on the customers, elevating the proof points here. Some of the most significant customers we have in the telco and financial services and healthcare and, and retail area are, you know, view this as a strategic weapon view, this is a huge competitive advantage, and it's helping them impact their business. That's really spring our success. We've, you know, we're, we're growing at an incredible clip here and it's just, it's a great time to have made those calls and those investments early on and kind of reaping the benefits. >>It's. Now I've always said, when we, since the first Hadoop summit, when Hortonworks came out of Yahoo and this whole community kind of burst open, you had to duke world. Now Riley runs at it's a whole different vibe of itself. This was look at the developer vibe. So I got to ask you, and we would have been a big fan. I mean, everyone has enough beachhead to be successful, not about map arbors Hortonworks or cloud air. And this is why I always kind of smile when everyone goes, oh, Cloudera or Hortonworks. I mean, they're two different animals at this point. It would do different things. If you guys were over here, everyone has their quote, swim lanes or beachhead is not a lot of super competition. Do you think, or is it going to be this way for awhile? What's your fork at some? At what point do you see more competition? 10 years out? I mean, Merv was talking a 10 year horizon for innovation. >>I think that the more people learn and understand about Hadoop, the more they'll appreciate these kind of set of capabilities that matter in production and post-production, and it'll migrate earlier. And as we, you know, focus on more developer tools like our sandbox, so people can easily get experienced and understand kind of what map are, is. I think we'll start to see a lot more understanding and momentum. >>Awesome. Jack Norris here, inside the cube CMO, Matt BARR, a very successful enterprise grade, a duke player, a leader in the space. Thanks for coming on. We really appreciate it. Right back after the short break you're live in Silicon valley, I had dupe December, 2014, the right back.

>>Hi everybody. We're back. This is Dave Volante from Wiki bond.org. We're live at strata in Santa Clara, California. This is Silicon angle TVs, continuous coverage of the strata conference. So Riley media or Raleigh media is a great partner of ours. And thanks to them for allowing us to be here. We've been going all week cause it's day three for us. I'm here with Jeff Kelly Wiki bonds that lead big data analysts. And we're here with Jack Norris. Who's the VP of marketing at Matt bar Jack. Welcome to the cube. Thank you, Dave. Thanks very much for coming on. And you know, we've been going all week. You guys are a great sponsor of ours. Thank you for the support. We really appreciate it. How's the show going for you? >>Great. A lot of attention, a lot of focus, a lot of discussion about Hadoop and big data. >>Yeah. So you guys getting a lot of traffic. I mean, it says I hear this 2,500 people here up from 1400 last year. So that's >>Yeah, we've had like five, six people deep in the, in the booth. So I think there's a lot of, a lot of interests. There's interesting. >>You know, when we were here last year, when you looked at the, the infrastructure and the competitive landscape, there wasn't a lot going on and just a very short time, that's completely changed. And you guys have had your hand in that. So, so that's good. Competition is a good thing, right? And, and obviously customers want choice, but so we want to talk about that a little bit. We want to talk about map bar, the kind of problems you're solving. So why don't we start there? What is map are all about? And you've got your own distribution of, of, of enterprise Hadoop. You make it Hadoop enterprise ready? Let's start there. >>Okay. Yeah, I mean, we invested heavily in creating a alternative distribution one that took the best of the open source community with the best of the map, our innovations, and really it's, it's about making Hadoop more applicable, broader use cases, more mission, critical support, you know, being able to sit in and work in a lights out data center environment. >>Okay. So what was the problem that you set out to solve? Why, why do, why do we need another distribution of Hadoop? Let me ask it that way. Get nice and close to. >>So there, there are some just big issues with, with the duke. >>One of those issues, let's talk about that. There's >>Some ease of use issues. There's some deep dependability issues. There's some, some performance. So, you know, let's take those in order right now. If you look at some of the distributions, Apache Hadoop, great technology, but it requires a programmer, right? To get access to the data it's through the Hadoop API, you can't really see the data. So there's a lot of focus of, you know, what do I do once the data's in there opening that up, providing a full file based access, right? So I can look at it and treat it like enterprise storage, see the data, use my standard tools, standard commands, you know, drag and drop from a file browser. You can do that with Matt bar. You can't do that with other districts >>Talking about mountain HDFS as a NFS correct >>Example. Correct. And then, and then just the underlying storage services. The fact that it's append only instead of full random read-write, you know, causes some, some issues. So, you know, that's some of the, the ease of use features. There's a whole lot. We could discuss there. Big picture for reliability. Dependability is there's a single point of failure, multiple single points of failure within Hadoop. So you risk data loss. So people have looked at Hadoop. Traditionally is, is batch oriented. Scratchpad right. We were out to solve that, right? We want to make sure that you can use it for mission critical data, that you don't have a risk of a data loss that you've got full high availability. You've got the full data protection in terms of snapshots and mirroring that you would expect with the enterprise products. >>It gets back to when you guys were, you know, thinking about doing this. I'm not even sure you were at the company at the time, but you, your DNA was there and you're familiar with it. So you guys saw this big data movement. You saw this at duke moon and you said, okay, this is cool. It's going to be big. And it's gonna take a long time for the community to fix all these problems. We can fix them. Now let's go do that. Is that the general discussion? Yeah. >>You know, I think, I think the what's different about this. This is the first open source package. The first open source project that's created a market. If you look at the other open source, you know, Linux, my SQL, et cetera, it was really late in the life cycle of a product. Everyone knew what the features were. It was about, you know, giving an alternative choice, better Unix. Your, your, the focus is on innovation and our founders, you know, have deep enterprise background or CTO was at Google and charge of big table, understands MapReduce at scale, spent time as chief software architect at Spinnaker, which was kind of the fastest clustered Nazanin on the planet. So recognize that the underlying layers of Hadoop needed some rearchitecture and needed some deep investment and to do that effectively and do that quickly required a whole lot of focus. And we thought that was the best way to go to market. >>Talk about the early validation from customers. Obviously you guys didn't just do this in a vacuum, I presume. So you went out and talked to some customers. Yeah. >>What sorts of conversations with customers, why we're in stealth mode? We're probably the loudest stealth >>As you were nodding. And I mean, what were they telling you at the time? Yeah, please go do this. >>The, what we address weren't secrets. I there've been gyrus for open for four or five years on, on these issues. >>Yeah. But at the same time, Jack, you've got this, you got this purist community out there that says, I don't want to, I don't want to rip out HDFS. You know, I want it to be pure. What'd you, what'd you say to those guys, you just say, okay, thank you. We, we understand you're not a prospect. >>And I think, I think that, you know, duke has a huge amount of momentum. And I think a lot of that momentum is that there isn't any risks to adopting Hadoop, right? It's not like the fractured no SQL market where there's 122 different entrance, which one's going to win. Hadoop's got the ecosystem. So when you say pure, it's about the API APIs, it's about making sure that if I create a MapReduce job, it's going to run an Apache. It's going to run a map bar. It's going to run on the other distributions. That's where I think that the heat and the focus is now to do that. You also have to have innovation occurring up and down the stack that that provides choice and alternatives for. >>So when I'm talking about purists, I don't, I agree with you the whole lock-in thing, which is the elephant in the room here. People will worry about lock-in >>Pun intended. >>No, no, but good one good catch. But so, but you're basically saying, Hey, where we're no more locked in than cloud era. Right. I mean, they've got their own >>Actually. I think we're less because it's so easy to get data in and out with our NFS. That there's probably less so, >>So, and I'm gonna come back to that. But so for instance, many, when I, when I say peers, I mean some users in ISV, some guys we've had on here, we had an Abby Mehta from Triceda on the other day, for instance, he's one who said, I just don't have time to mess with that stuff and figure out all that API integration. I mean, there are people out there that just don't want to go that route. Okay. But, but you're saying I'm, I'm inferring this plenty who do right. >>And the, and by the API route, I want to make sure I understand what you're saying. You >>Talked about, Hey, it's all about the API integration. It's not >>About, it's not the, it it's about the API APIs being consistent, a hundred percent compatible. Right. So if I, you know, write a program, that's, that's going after HDFS and the HDFS API, I want to make sure that that'll run on other distributions. Right. >>And that's your promise. Yeah. Okay. All right. So now where I was going with this was th again, there are some peers to say, oh, I just don't want to mess with all that. Now let's talk about what that means to mess with all that. So comScore was a big, high profile case study for you guys. They, they were cloud era customer. They basically, in my understanding is a couple of days migrated from Cloudera to Mapbox. And the impetus was, let's talk about that. Why'd they do that >>Performance data protection, ease of use >>License fee issues. There was some license issues there as well, right? The, the, your, your maintenance pricing was more attractive. Is that true? Or >>I read more mainly about price performance and reliability, and, you know, they tested our stuff at work real well in a test environment, they put it in production environment. Didn't actually tell all their users, they had one guys debug the software for half a day because something was wrong. It finished so quickly. >>So, so it took him a couple of days to migrate and then boom, >>Boom. And they've, they handle about 30 billion objects a day. So there, you know, the use of that really high performance support for, for streaming data flows, you know, they're talking about, they're doing forecasts and insights into web behavior, and, you know, they w the earlier they can do that, the better off they are. So >>Greg, >>So talk about the implications of, of your approach in terms of the customer base. So I'm, I'm imagining that your customers are more, perhaps advanced than a lot of your typical Hadoop users who are just getting started tinkering with Hadoop. Is it fair to say, you know, your customers know what they want and they want performance and they want it now. And they're a little more advanced than perhaps some of the typical early adopters. >>We've got people to go to our website and download the free version. And some of them are just starting off and getting used to Hadoop, but we did specifically target those very experienced Hadoop users that, you know, we're kind of, you know, stubbing their toes on, on the issues. And so they're very receptive to the message of we've made it faster. We've made it more reliable, you know, we've, we've added a lot of ease of use to the, to the Hindu. >>So I found this, let me interrupt, go back to what I was saying before is I found this comment that I found online from Mike Brown comScore. Skipio I presume you mean, he said comScore's map our direct access NFS feature, which exposes a duke distributed file system data as NFS files can then be easily mounted, modified, or overwritten. So that's a data access simplification. You also said we could capitalize on the purchase of map bar with an annual maintenance charge versus a yearly cost per node. NFS allowed our enterprise systems to easily access the data in the cluster. So does that make sense to you that, that enterprise of that annual maintenance charge versus yearly cost per node? I didn't get that. >>Oh, I think he's talking about some, some organizations prefer to do a perpetual license versus a subscription model that's >>Oh, okay. So the traditional way of licensing software >>And that, that you have to do it basically reinforces the fact that we've really invested in have kind of a, a product, you know, orientation rather than just services on top of, of some opensource. >>Okay. So you go in, you license it and then yeah. Perpetual license. >>Then you can also start with the free edition that does all the performance NFS support kick the tires >>Before you buy it. Sorry. Sorry, Jeff. Sorry to interrupt. No, no problem >>At all. So another topic, a lot of interest is security making a dupe enterprise ready. One of the pillars, there is security, making sure access controls, for instance, making sure let's talk about how you guys approach that and maybe how you differentiate from some of the other vendors out there, or the other >>Full Kerberos support. We Lincoln to enterprise standards for access eldap, et cetera. We leveraged the Linux, Pam security, and we also provide volume control. So, you know, right now in Hindu in Apache to dupe other distributions, you put policies at the file level or the entire cluster. And we see many organizations having separate physical clusters because of that limitation, right? And we'd provide volume. So you can define a volume. And in that volume control, access control, administrative privileges data protection class, and, you know, in a sense kind of segregate that content. And that provides a lot of, a lot of control and a lot more, you know, security and protection and separation of data. >>That scenario, the comScore scenario, common where somebody's moving off an existing distribution onto a map are, or, or you more going, going, seeing demand from new customers that are saying, Hey, what's this big data thing I really want to get into it. How's it shake out there >>Right now? There's this huge pent up demand for these features. And we're seeing a lot of people that have run on other distributions switched to map our >>A little bit of everything. How about, can you talk a little bit about your, your channel? You go to market strategy, maybe even some of your ecosystem and partnerships in the little time. >>Sure. So EMC is a big partner of the EMC Greenplum Mr. Edition is basically a map R you can start with any of our additions and upgrade to that. Greenplum with just a licensed key that gives us worldwide service and support. It's been a great partnership. >>We hear a lot of proof of concepts out there >>For, yeah. And then it just hit the news news today about EMC's distribution, Mr. Distribution being available with UCS Cisco's ECS gear. So now that's further expanded the, the footprint that we have about. >>Okay. So you're the EMC relationship. Anything else that you can share with us? >>We have other announcements coming out and >>Then you want to pre-announce in the queue. >>Oops. Did I let that slip >>It's alive? So be careful. And so, in terms of your, your channel strategy, you guys mostly selling direct indirect combination, >>It's it? It, it's kind of an indirect model through these, these large partners with a direct assist. >>Yeah. Okay. So you guys come in and help evangelize. Yep. Excellent. All right. Do you have anything else before we gotta got a roll here? >>Yeah, I did wonder if you could talk a little bit about, you mentioned EMC Greenplum so there's a lot of talk about the data warehouse market, the MPB data warehouses, versus a Hadoop based on that relationship. I'm assuming that Matt BARR thinks well, they're certainly complimentary. Can you just touch on that? And, you know, as opposed to some who think, well, Hadoop is going to be the platform where we go, >>Well, th th there's just, I mean, if you look at the typical organization, they're just really trying to get their, excuse me, their arms around a lot of this machine generated content, this, you know, unstructured data that just growing like wildfire. So there's a lot of Paducah specific use cases that are being rolled out. They're also kind of data lakes, data, oceans, whatever you want to call it, large pools where that information is then being extracted and loaded into data warehouses for further analysis. And I think the big pivot there is if it's well understood what the issue is, you define the schema, then there's a whole host of, of data warehouse applications out there that can be deployed. But there's many things where you don't really understand that yet having to dupe where you don't need to find a schema a is a, is a big value, >>Jack, I'm sorry. We have to go run a couple of minutes behind. Thank you very much for coming on the cube. Great story. Good luck with everything. And sounds like things are really going well and market's heating up and you're in the right place at the right time. So thank you again. Thank you to Jeff. And we'll be right back everybody to the strata conference live in Santa Clara, California, right after this word from our.