>> Narrator: From Berlin, Germany, it's theCUBE! Covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Well, hello. Welcome to theCUBE. I'm James Kobielus. I'm lead analyst at Wikibon for big data analytics. Wikibon, of course, is the analyst team inside of SiliconANGLE Media. One of our core offerings is theCUBE and I'm here with Joe Morrissey. Joe is the VP for International at Hortonworks and Hortonworks is the host of Dataworks Summit. We happen to be at Dataworks Summit 2018 in Berlin! Berlin, Germany. And so, Joe, it's great to have you. >> Great to be here! >> We had a number of conversations today with Scott Gnau and others from Hortonworks and also from your customer and partners. Now, you're International, you're VP for International. We've had a partner of yours from South Africa on theCUBE today. We've had a customer of yours from Uruguay. So there's been a fair amount of international presence. We had Munich Re from Munich, Germany. Clearly Hortonworks is, you've been in business as a company for seven years now, I think it is, and you've established quite a presence worldwide, I'm looking at your financials in terms of your customer acquisition, it just keeps going up and up so you're clearly doing a great job of bringing the business in throughout the world. Now, you've told me before the camera went live that you focus on both Europe and Asia PACS, so I'd like to open it up to you, Joe. Tell us how Hortonworks is doing worldwide and the kinds of opportunities you're selling into. >> Absolutely. 2017 was a record year for us. We grew revenues by over 40% globally. I joined to lead the internationalization of the business and you know, not a lot of people know that Hortonworks is actually one of the fastest growing software companies in history. We were the fastest to get to $100 million. Also, now the fastest to get to $200 million but the majority of that revenue contribution was coming from the United States. When I joined, it was about 15% of international contribution. By the end of 2017, we'd grown that to 31%, so that's a significant improvement in contribution overall from our international customer base even though the company was growing globally at a very fast rate. >> And that's also not only fast by any stretch of the imagination in terms of growth, some have said," Oh well, maybe Hortonworks, "just like Cloudera, maybe they're going to plateau off "because the bloom is off the rose of Hadoop." But really, Hadoop is just getting going as a market segment or as a platform but you guys have diversified well beyond that. So give us a sense for going forward. What are your customers? What kind of projects are you positioning and selling Hortonworks solutions into now? Is it a different, well you've only been there 18 months, but is it shifting towards more things to do with streaming, NiFi and so forth? Does it shift into more data science related projects? Coz this is worldwide. >> Yeah. That's a great question. This company was founded on the premise that data volumes and diversity of data is continuing to explode and we believe that it was necessary for us to come and bring enterprise-grade security and management and governance to the core Hadoop platform to make it really ready for the enterprise, and that's what the first evolution of our journey was really all about. A number of years ago, we acquired a company called Onyara, and the logic behind that acquisition was we believe companies now wanted to go out to the point of origin, of creation of data, and manage data throughout its entire life cycle and derive pre-event as well as post-event analytical insight into their data. So what we've seen as our customers are moving beyond just unifying data in the data lake and deriving post-transaction inside of their data. They're now going all the way out to the edge. They're deriving insight from their data in real time all the way from the point of creation and getting pre-transaction insight into data as well so-- >> Pre-transaction data, can you define what you mean by pre-transaction data. >> Well, I think if you look at it, it's really the difference between data in motion and data at rest, right? >> Oh, yes. >> A specific example would be if a customer walks into the store and they've interacted in the store maybe on social before they come in or in some other fashion, before they've actually made the purchase. >> Engagement data, interaction data, yes. >> Engagement, exactly. Exactly. Right. So that's one example, but that also extends out to use cases in IoT as well, so data in motion and streaming data, as you mentioned earlier since become a very, very significant use case that we're seeing a lot of adoption for. Data science, I think companies are really coming to the realization that that's an essential role in the organization. If we really believe that data is the most important asset, that it's the crucial asset in the new economy, then data scientist becomes a really essential role for any company. >> How do your Asian customers' requirements differ, or do they differ from your European cause European customers clearly already have their backs against the wall. We have five weeks until GDPR goes into effect. Do many of your Asian customer, I'm sure a fair number sell into Europe, are they putting a full court, I was going to say in the U.S., a full court press on complying with GDPR, or do they have equivalent privacy mandates in various countries in Asia or a bit of both? >> I think that one of the primary drivers I see in Asia is that a lot of companies there don't have the years of legacy architecture that European companies need to contend with. In some cases, that means that they can move towards next generation data-orientated architectures much quicker than European companies have. They don't have layers of legacy tech that they need to sunset. A great example of that is Reliance. Reliance is the largest company in India, they've got a subsidiary called GO, which is the fastest growing telco in the world. They've implemented our technology to build a next-generation OSS system to improve their service delivery on their network. >> Operational support system. >> Exactly. They were able to do that from the ground up because they formed their telco division around being a data-only company and giving away voice for free. So they can in some extent, move quicker and innovate a little faster in that regards. I do see much more emphasis on regulatory compliance in Europe than I see in Asia. I do think that GDPR amongst other regulations is a big driver of that. The other factor though I think that's influencing that is Cloud and Cloud strategy in general. What we've found is that, customers are drawn to the Cloud for a number of reasons. The economics sometimes can be attractive, the ability to be able to leverage the Cloud vendors' skills in terms of implementing complex technology is attractive, but most importantly, the elasticity and scalability that the Cloud provides us, hugely important. Now, the key concern for customers as they move to the Cloud though, is how do they leverage that as a platform in the context of an overall data strategy, right? And when you think about what a data strategy is all about, it all comes down to understanding what your data assets are and ensuring that you can leverage them for a competitive advantage but do so in a regulatory compliant manner, whether that's data in motion or data at rest. Whether it's on-prem or in the Cloud or in data across multiple Clouds. That's very much a top of mind concern for European companies. >> For your customers around the globe, specifically of course, your area of Europe and Asia, what percentage of your customers that are deploying Hortonworks into a purely public Cloud environment like HDInsight and Microsoft Azure or HDP inside of AWS, in a public Cloud versus in a private on-premises deployment versus in a hybrid public-private multi Cloud. Is it mostly on-prem? >> Most of our business is still on-prem to be very candid. I think almost all of our customers are looking at migrating, some more close to the Cloud. Even those that had intended to have a Cloud for a strategy have now realized that not all workloads belong in the Cloud. Some are actually more economically viable to be on-prem, and some just won't ever be able to move to the Cloud because of regulation. In addition to that, most of our customers are telling us that they actually want Cloud optionality. They don't want to be locked in to a single vendor, so we very much view the future as hybrid Cloud, as multi Cloud, and we hear our customers telling us that rather than just have a Cloud strategy, they need a data strategy. They need a strategy to be able to manage data no matter where it lives, on which tier, to ensure that they are regulatory compliant with that data. But then to be able to understand that they can secure, govern, and manage those data assets at any tier. >> What percentage of your deals involve a partner? Like IBM is a major partner. Do you do a fair amount of co-marketing and joint sales and joint deals with IBM and other partners or are they mostly Hortonworks-led? >> No, partners are absolutely critical to our success in the international sphere. Our partner revenue contribution across EMEA in the past year grew, every region grew by over 150% in terms of channel contribution. Our total channel business was 28% of our total, right? That's a very significant contribution. The growth rate is very high. IBM are a big part of that, as are many other partners. We've got, the very significant reseller channel, we've got IHV and ISV partners that are critical to our success also. Where we're seeing the most impact with with IBM is where we go to some of these markets where we haven't had a presence previously, and they've got deep and long-standing relationships and that helps us accelerate time to value with our customers. >> Yeah, it's been a very good and solid partnership going back several years. Well, Joe, this is great, we have to wrap it up, we're at the end of our time slot. This has been Joe Morrissey who is the VP for International at Hortonworks. We're on theCUBE here at Dataworks Summit 2018 in Berlin, and want to thank you all for watching this segment and tune in tomorrow, we'll have a full slate of further discussions with Hortonworks, with IBM and others tomorrow on theCUBE. Have a good one. 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>> Coming back when we were a DOS spreadsheet company. I did a short stint at Microsoft and then joined Frank Quattrone when he spun out of Morgan Stanley to create what would become the number three tech investment (upbeat music) >> Host: Live from mid-town Manhattan, it's theCUBE covering the BigData New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (upbeat electronic music) >> Welcome back, everyone. We're here, live, on day two of our three days of coverage of BigData NYC. This is our event that we put on every year. It's our fifth year doing BigData NYC in conjunction with Hadoop World which evolved into Strata Conference, which evolved into Strata Hadoop, now called Strata Data. Probably next year will be called Strata AI, but we're still theCUBE, we'll always be theCUBE and this our BigData NYC, our eighth year covering the BigData world since Hadoop World. And then as Hortonworks came on we started covering Hortonworks' data summit. >> Arun: DataWorks Summit. >> DataWorks Summit. Arun Murthy, my next guest, Co-Founder and Chief Product Officer of Hortonworks. Great to see you, looking good. >> Likewise, thank you. Thanks for having me. >> Boy, what a journey. Hadoop, years ago, >> 12 years now. >> I still remember, you guys came out of Yahoo, you guys put Hortonworks together and then since, gone public, first to go public, then Cloudera just went public. So, the Hadoop World is pretty much out there, everyone knows where it's at, it's got to nice use case, but the whole world's moved around it. You guys have been, really the first of the Hadoop players, before ever Cloudera, on this notion of data in flight, or, I call, real-time data but I think, you guys call it data-in-motion. Batch, we all know what Batch does, a lot of things to do with Batch, you can optimize it, it's not going anywhere, it's going to grow. Real-time data-in-motion's a huge deal. Give us the update. >> Absolutely, you know, we've obviously been in this space, personally, I've been in this for about 12 years now. So, we've had a lot of time to think about it. >> Host: Since you were 12? >> Yeah. (laughs) Almost. Probably look like it. So, back in 2014 and '15 when we, sort of, went public and we're started looking around, the thesis always was, yes, Hadoop is important, we're going to love you to manage lots and lots of data, but a lot of the stuff we've done since the beginning, starting with YARN and so on, was really enable the use cases beyond the whole traditional transactions and analytics. And Drop, our CO calls it, his vision's always been we've got to get into a pre-transactional world, if you will, rather than the post-transactional analytics and BIN and so on. So that's where it started. And increasingly, the obvious next step was to say, look enterprises want to be able to get insights from data, but they also want, increasingly, they want to get insights and they want to deal with it in real-time. You know while you're in you shopping cart. They want to make sure you don't abandon your shopping cart. If you were sitting at at retailer and you're on an island and you're about to walk away from a dress, you want to be able to do something about it. So, this notion of real-time is really important because it helps the enterprise connect with the customer at the point of action, if you will, and provide value right away rather than having to try to do this post-transaction. So, it's been a really important journey. We went and bought this company called Onyara, which is a bunch of geeks like us who started off with the government, built this batching NiFi thing, huge community. Its just, like, taking off at this point. It's been a fantastic thing to join hands and join the team and keep pushing in the whole streaming data style. >> There's a real, I don't mean to tangent but I do since you brought up community I wanted to bring this up. It's been the theme here this week. It's more and more obvious that the community role is becoming central, beyond open-source. We all know open-source, standing on the shoulders before us, you know. And Linux Foundation showing code numbers hitting up from $64 million to billions in the next five, ten years, exponential growth of new code coming in. So open-source certainly blew me. But now community is translating to things you start to see blockchain, very community based. That's a whole new currency market that's changing the financial landscape, ICOs and what-not, that's just one data point. Businesses, marketing communities, you're starting to see data as a fundamental thing around communities. And certainly it's going to change the vendor landscape. So you guys compare to, Cloudera and others have always been community driven. >> Yeah our philosophy has been simple. You know, more eyes and more hands are better than fewer. And it's been one of the cornerstones of our founding thesis, if you will. And you saw how that's gone on over course of six years we've been around. Super-excited to have someone like IBM join hands, it happened at DataWorks Summit in San Jose. That announcement, again, is a reflection of the fact that we've been very, very community driven and very, very ecosystem driven. >> Communities are fundamentally built on trust and partnering. >> Arun: Exactly >> Coding is pretty obvious, you code with your friends. You code with people who are good, they become your friends. There's an honor system among you. You're starting to see that in the corporate deals. So explain the dynamic there and some of the successes that you guys have had on the product side where one plus one equals more than two. One plus one equals five or three. >> You know IBM has been a great example. They've decided to focus on their strengths which is around Watson and machine learning and for us to focus on our strengths around data management, infrastructure, cloud and so on. So this combination of DSX, which is their data science work experience, along with Hortonworks is really powerful. We are seeing that over and over again. Just yesterday we announced the whole Dataplane thing, we were super excited about it. And now to get IBM to say, we'll get in our technologies and our IP, big data, whether it's big Quality or big Insights or big SEQUEL, and the word has been phenomenal. >> Well the Dataplane announcement, finally people who know me know that I hate the term data lake. I always said it's always been a data ocean. So I get redemption because now the data lakes, now it's admitting it's a horrible name but just saying stitching together the data lakes, Which is essentially a data ocean. Data lakes are out there and you can form these data lakes, or data sets, batch, whatever, but connecting them and integrating them is a huge issue, especially with security. >> And a lot of it is, it's also just pragmatism. We start off with this notion of data lake and say, hey, you got too many silos inside the enterprise in one data center, you want to put them together. But then increasingly, as Hadoop has become more and more mainstream, I can't remember the last time I had to explain what Hadoop is to somebody. As it has become mainstream, couple things have happened. One is, we talked about streaming data. We see all the time, especially with HTF. We have customers streaming data from autonomous cars. You have customers streaming from security cameras. You can put a small minify agent in a security camera or smart phone and can stream it all the way back. Then you get into physics. You're up against the laws of physics. If you have a security camera in Japan, why would you want to move it all the way to California and process it. You'd rather do it right there, right? So with this notion of a regional data center becomes really important. >> And that talks to the Edge as well. >> Exactly, right. So you want to have something in Japan that collects all of the security cameras in Tokyo, and you do analysis and push what you want back here, right. So that's physics. The other thing we are increasingly seeing is with data sovereignty rules especially things like GDPR, there's now regulation reasons where data has to naturally stay in different regions. Customer data from Germany cannot move to France or visa versa, right. >> Data governance is a huge issue and this is the problem I have with data governance. I am really looking for a solution so if you can illuminate this it would be great. So there is going to be an Equifax out there again. >> Arun: Oh, for sure. >> And the problem is, is that going to force some regulation change? So what we see is, certainly on the mugi bond side, I see it personally is that, you can almost see that something else will happen that'll force some policy regulation or governance. You don't want to screw up your data. You also don't want to rewrite your applications or rewrite you machine learning algorithms. So there's a lot of waste potential by not structuring the data properly. Can you comment on what's the preferred path? >> Absolutely, and that's why we've been working on things like Dataplane for almost a couple of years now. We is to say, you have to have data and policies which make sense, given a context. And the context is going to change by application, by usage, by compliance, by law. So, now to manage 20, 30, 50 a 100 data lakes, would it be better, not saying lakes, data ponds, >> [Host} Any Data. >> Any data >> Any data pool, stream, river, ocean, whatever. (laughs) >> Jacuzzis. Data jacuzzis, right. So what you want to do is want a holistic fabric, I like the term, you know Forrester uses, they call it the fabric. >> Host: Data fabric. >> Data fabric, right? You want a fabric over these so you can actually control and maintain governance and security centrally, but apply it with context. Last not least, is you want to do this whether it's on frame or on the cloud, or multi-cloud. So we've been working with a bank. They were probably based in Germany but for GDPR they had to stand up something in France now. They had French customers, but for a bunch of new reasons, regulation reasons, they had to sign up something in France. So they bring their own data center, then they had only the cloud provider, right, who I won't name. And they were great, things are working well. Now they want to expand the similar offering to customers in Asia. It turns out their favorite cloud vendor was not available in Asia or they were not available in time frame which made sense for the offering. So they had to go with cloud vendor two. So now although each of the vendors will do their job in terms of giving you all the security and governance and so on, the fact that you are to manage it three ways, one for OnFrame, one for cloud vendor A and B, was really hard, too hard for them. So this notion of a fabric across these things, which is Dataplane. And that, by the way, is based by all the open source technologies we love like Atlas and Ranger. By the way, that is also what IBM is betting on and what the entire ecosystem, but it seems like a no-brainer at this point. That was the kind of reason why we foresaw the need for something like a Dataplane and obviously couldn't be more excited to have something like that in the market today as a net new service that people can use. >> You get the catalogs, security controls, data integration. >> Arun: Exactly. >> Then you get the cloud, whatever, pick your cloud scenario, you can do that. Killer architecture, I liked it a lot. I guess the question I have for you personally is what's driving the product decisions at Hortonworks? And the second part of that question is, how does that change your ecosystem engagement? Because you guys have been very friendly in a partnering sense and also very good with the ecosystem. How are you guys deciding the product strategies? Does it bubble up from the community? Is there an ivory tower, let's go take that hill? >> It's both, because what typically happens is obviously we've been in the community now for a long time. Working publicly now with well over 1,000 customers not only puts a lot of responsibility on our shoulders but it's also very nice because it gives us a vantage point which is unique. That's number one. The second one we see is being in the community, also we see the fact that people are starting to solve the problems. So it's another elementary for us. So you have one as the enterprise side, we see what the enterprises are facing which is kind of where Dataplane came in, but we also saw in the community where people are starting to ask us about hey, can you do multi-cluster Atlas? Or multi-cluster Ranger? Put two and two together and say there is a real need. >> So you get some consensus. >> You get some consensus, and you also see that on the enterprise side. Last not least is when went to friends like IBM and say hey we're doing this. This is where we can position this, right. So we can actually bring in IGSC, you can bring big Quality and bring all these type, >> [Host} So things had clicked with IBM? >> Exactly. >> Rob Thomas was thinking the same thing. Bring in the power system and the horsepower. >> Exactly, yep. We announced something, for example, we have been working with the power guys and NVIDIA, for deep learning, right. That sort of stuff is what clicks if you're in the community long enough, if you have the vantage point of the enterprise long enough, it feels like the two of them click. And that's frankly, my job. >> Great, and you've got obviously the landscape. The waves are coming in. So I've got to ask you, the big waves are coming in and you're seeing people starting to get hip with the couple of key things that they got to get their hands on. They need to have the big surfboards, metaphorically speaking. They got to have some good products, big emphasis on real value. Don't give me any hype, don't give me a head fake. You know, I buy, okay, AI Wash, and people can see right through that. Alright, that's clear. But AI's great. We all cheer for AI but the reality is, everyone knows that's pretty much b.s. except for core machine learning is on the front edge of innovation. So that's cool, but value. [Laughs] Hey I've got the integrate and operationalize my data so that's the big wave that's coming. Comment on the community piece because enterprises now are realizing as open source becomes the dominant source of value for them, they are now really going to the next level. It used to be like the emerging enterprises that knew open source. The guys will volunteer and they may not go deeper in the community. But now more people in the enterprises are in open source communities, they are recruiting from open source communities, and that's impacting their business. What's your advice for someone who's been in the community of open source? Lessons you've learned, what is the best practice, from your standpoint on philosophy, how to build into the community, how to build a community model. >> Yeah, I mean, the end of the day, my best advice is to say look, the community is defined by the people who contribute. So, you get advice if you contribute. Which means, if that's the fundamental truth. Which means you have to get your legal policies and so on to a point that you can actually start to let your employees contribute. That kicks off a flywheel, where you can actually go then recruit the best talent, because the best talent wants to stand out. Github is a resume now. It is not a word doc. If you don't allow them to build that resume they're not going to come by and it's just a fundamental truth. >> It's self governing, it's reality. >> It's reality, exactly. Right and we see that over and over again. It's taken time but it as with things, the flywheel has changed enough. >> A whole new generation's coming online. If you look at the young kids coming in now, it is an amazing environment. You've got TensorFlow, all this cool stuff happening. It's just amazing. >> You, know 20 years ago that wouldn't happen because the Googles of the world won't open source it. Now increasingly, >> The secret's out, open source works. >> Yeah, (laughs) shh. >> Tell everybody. You know they know already but, This is changing some of the how H.R. works and how people collaborate, >> And the policies around it. The legal policies around contribution so, >> Arun, great to see you. Congratulations. It's been fun to watch the Hortonworks journey. I want to appreciate you and Rob Bearden for supporting theCUBE here in BigData NYC. If is wasn't for Hortonworks and Rob Bearden and your support, theCUBE would not be part of the Strata Data, which we are not allowed to broadcast into, for the record. O'Reilly Media does not allow TheCube or our analysts inside their venue. They've excluded us and that's a bummer for them. They're a closed organization. But I want to thank Hortonworks and you guys for supporting us. >> Arun: Likewise. >> We really appreciate it. >> Arun: Thanks for having me back. >> Thanks and shout out to Rob Bearden. Good luck and CPO, it's a fun job, you know, not the pressure. I got a lot of pressure. A whole lot. >> Arun: Alright, thanks. >> More Cube coverage after this short break. (upbeat electronic music)

>> Voiceover: On the Ground, presented by The Cube. Here's your host John Furrier. (techno music) >> Hello, everyone. Welcome to a special On the Ground executive interview with Rob Bearden, the CEO of Hortonworks. I'm John Furrier with The Cube. Rob, welcome to this On the Ground. >> Thank you. >> So I got to ask you, you're five years old this year, your company Hortonworks in June, have Hadoop Summit coming up, what a magical run. You guys went public. Give us a quick update on Hortonworks and what's going on. The five-year birthday, any special plans? >> Well, we're going to actually host the 10-year birthday party of Hadoop, which is you know, started at Yahoo! and open-source community. So everyone's invited. Hopefully you'll be able to make it as well. We've accomplished a lot in the last five years. We've grown to over 1000 employees, over 900 customers. This year is our first full year of being a public company, and the street has us at $265 million dollars in billings. So tremendous progress has happened and we've seen the entire data architecture begin to re-platform around Hadoop now. >> CEOs across the globe are facing profound challenges, data, cloud, mobile, obviously this digital transformation. What are you seeing our there as you talk to your customers? >> Well they view that the digital transformation is a massive opportunity for value creation, for that enterprise. And they realize that they can really shift their business models from being very reactive post-transaction to actually being able to consolidate all of the new paradigm data with the existing transaction data and actually get to a very pro-active model pre-transaction. And so they understand their customer's patterns. They understand the kinds of things that their customers want to buy before they ever engage in the procurement process. And they can make better and more compelling offers at better price points and be able to serve their customers better, and that's really the transformation that's happening and they realize the value of that creation between them and their customer. >> And one of the exciting things about The Cube is we go to all these different industry events and you were speaking last week at an event where data is at the center of the value proposition around digital transformation, and that's really been the key trend that we've been seeing consistently, that buzz word digital transformation. What does that mean to you? Because this is coming up over and over again around this digital platform, digital weathers, digital media or digital engagement. It's all around data. What's your thoughts and what is from your perspective digital transformation? >> Well, it's about being able to derive value from your data and be able to take that value back to your customers under your supply chain, and to be able to create a completely new engagement with how you're managing your interaction with your customers and your supply chain from the data that they're generating and the data that you have about them. >> When you talk to CEOs and people in the business out in the field, how much of this digital transformation do you see as real in terms of progress, real progress? In terms of total transitions, or is it just being talked about now? What's your progress bar meter? How would you peg this trend? >> I would say we're at four and I believe we'll be at six by the end of 2016. And it's one of the biggest movements I've seen since the '90s and ERP, because it's so transformational into the business model by being able to transform the data that we have about our collective entity and our collective customer and collective supply chain, and be able to apply predictive and real-time interactions against that data as events and occurrences are happening, and to be able to quickly offer products and services, and the velocity that that creates to modernization and the value creation back is at a pace that's never been able to happen. And they've really understood the importance of doing that or being disintermediated in their existing spaces. >> You mention ERP, it kind of shows our age, but I'll ask the question. Back in the '90s ERP, CRM, these were processes that were well known, that people automated with technology which was at that time unknown. You got a riser-client server technology, local area networking, TCP IP was emerging, so you got some unknown technology stuff happening, but known processes that were being automated and hence saw that boom. Now you mention today, it's interesting because Peter Burris at Wikibon's thesis says today the processes are unknown and the technology's known, so there's now a new dynamic. It's almost flipped upside-down where this digital transformation is exact opposite. IoT is a great use case where all these unknown things are coming into the enterprise that are value opportunities. Get the technology knows, so now the challenge is how to use technology, to deploy it, and be agile to capture and automate these future and/or real-time unknown processes. Your thoughts on that premise. >> The answers are buried in the data, is the great news, and so the technology as you said is there, and you have these new, unknown processes through Internet of Things, the new paradigm data sets with sensors and clickstream and mobile data. And the good news is they generate the data and we can apply technology to the data through AI and machine learning to really make sure that we understand how to transform the value out of that, out of those data sets. >> So how does IT deal with this? 'Cause going back 30 years IT was a clear line of sight, again, automating those known processes. Now you have unknown opportunities, but you have to be in a position for that. Call that cloud, call that DevOps, call that data driven, whatever the metaphor is. People are being agile, be ready for it. How is that different now and what is the future of data in that paradigm? And how does a customer come to grips and rationalize this notion of I need a clear line of sight of the value, not knowing what the processes is about data. What should they be doing? >> Well, we don't know the processes necessarily, per se, but we do know what the data is telling us because we can bring all that data under management. We can apply the right kind of algorithms, the right kind of tools on it, to give us the outcomes that we want and have the ability to monetize and unlock that value very quickly. >> Hortonworks architecture is kind of designed now at the last Hadoop Summit in Dublin. We heard about the platform. Your architecture's going beyond Hadoop, and it says Hadoop Summit and Hadoop was the key to big data. Going beyond Hadoop means other things. What does that mean for the customer? Because now they're seeing these challenges. How does Hortonworks describe that and what value do you bring to those customers? >> Big data was about data at rest and being able to drive the transformation that it has, being able to consolidate all the transactional platforms into central data architecture. Being able to bring all the new paradigm data sets to the mobile, the clickstream, the IoT data, and bring that together and be able to really transition from being reactive post-transaction to be able to be predictive and interactive pre-transaction. And that's a very, very powerful value proposition and you create a lot of value doing that, but what's really learned through that process is in the digital transformation journey, that actually the further upstream that we can get to engaging with the data, even if we can get to it at the point of origination at the furthest edge, at the point of center, at the actual time of clickstream and we can engage with that data as those events and occurrences are happening and we can process against those events as their happening, it creates higher levels of value. So from the Hortonworks platform we have the ability to manage data at rest with Hadoop, as well as data in motion with the Hortonworks data flow platform. And our view is that we must be able to engage with all the data all the time. And so we bring the platforms to bring data under management from the point of origination all the way through as it's in motion, and to the point it comes at rest and be able to aggregate those interactions through the entire process. >> It's interesting, you mention real-time, and one of the ideas of Hadoop was it was always going to be a data warehouse killer, 'cause it makes a lot of sense. You can store the data. It's unstructured data and you can blend in structured on top of that and build on top of that. Has that happened? And does real-time kind of change that equation? Because there's still a role for a data warehouse. If someone has an investment are they being modernized? Clear that up for me because I just can't kind of rationalize that yet. Data warehouses are old, the older ones, but they're not going away any time soon from what we're hearing. Your thoughts as Hadoop as the data warehouse killer. >> Yeah, well, our strategy from day one has never been to go in and disintermediate any of the existing platforms or any of the existing applications or services. In fact, to the contrary. What we wanted to do and have done from day one is be able to leverage Hadoop as an extension of those data platforms. The DW architecture has limitations to it in terms of how much data pragmatically and economically is really viable to go into the data warehouse. And so our model says let's bring more data under management as an extension to the existing data warehouses and give the existing data warehouses the ability to have a more holistic view of data. Now I think the next generation of evolution is happening right now and the enterprise is saying that's great. We're able to get more value longer from our existing data warehouse and tools investment by bringing more data under management, leveraging a combined architecture of Hadoop and data warehouse. But now they're trying to redefine really what does the data warehouse of the future look like, and it's really about how we make decisions, right? And at what point do we make decisions because in the world of DW today it assumes that data's aggregated post-transaction, right? In the new world of data architecture that's across the IT landscape, it says we want to engage with data from the point it's originated, and we want to be able to process and make decisions as events and as occurrences and as opportunities arise before that transaction potentially ever happens. And so the data warehouse of the future is much different in terms of how and when a decision's made and when that data's processed. And in many cases it's pre-transaction versus post-transaction. >> Well also I would just add, and I want to get your thoughts on this, real-time, 'cause now in the moment at the transaction we now have cloud resources and potentially other resources that could become available. Why even go to the data warehouses? So how has real-time changed the game? 'Cause data in motion kind of implies real-time whether it's IoT or some sort of bank transaction or something else. How has real-time changed the game? >> Well, it's at what point can we engage with the customer, but what it really has established is the data has to be able to be processed whether it be on Prim, in the cloud, or in a hybrid architecture. And we can't be constrained by where the data's processed. We need to be able to take the processing to the data versus having to wait for the data to come to the processing. And I think that's the very powerful part of cloud, the on Prim, and software to find networking, and when you bring all of those platforms together, you get the ability to have a very powerful and elastic processing capability at any point in the life cycle of the data. And we've never been able to put all those pieces together on an economically viable model. >> So I got to ask you, you guys are five years old in June, Hadoop's only 10 years old. Still young, still kind of in the early days, but yet you guys are public company. How are you guys looking at the growth strategy for you guys? 'Cause the trend is for people to go private. You guys went public. You're out in the open. Certainly your competitor Cloud ARIS is private, but people can get that they're kind of behind the curtain. Some say public with a $3 billion dollar graduation, but for the most part you're public. So the question is how are you guys going to sustain the growth? What is the growth strategy? What's your innovation strategy? >> Well if you look at the companies that are going private, those are the companies that are the older platforms, the older technologies, in a very mature market that have not been able to innovate those core platforms and they sort of reached their maturity cycle, and I think going private gives them the ability to do that innovation, maybe change their licensing model, the subscription, and make some of the transformations they need to make. I have no doubt they'll be very successful doing that. Our situation's much different. As the modern IT landscape is re-architecting itself almost across every layer. If you look at what's happening in the networking layer going to SDN. Certainly in our space with data and it's moving away from just transactional siloed environments to central data architectures and next generation data platforms. And being able to go all the way out to the edge and bring data under management through the entire movement cycle. We're in a market that we're able to innovate rapidly. Not only in terms of the architecture of the data platform being able to bring batch, real-time applications together simultaneously on a central data set and consolidate all of the data, but also then be able to move out and do the data in motion and be able to control an entire life cycle. There's a tremendous amount of innovation that's going to happen there, and these are significant growth markets. Both the data in motion and the data at rest market. The data at rest market's a $50 billion dollar marketplace. The data in motion market is a $1 trillion dollar TAM. So when you look at the massive opportunity to create value in these high growth markets, in the ability to innovate and create the next generation data platforms, there's a lot of room for growth and a lot of room for scale. And that's exactly why you should be public when you're going though these large growth markets in a space that's re-platforming, because the CIO wants to understand and have transparent visibility into their platform partners. They want to know how you're doing. Are you executing the plan? Or are you hiding behind a facade of one perception or another. >> Or pivoting or some sort of re-architecture. >> Right, so I think it's very appropriate in a high growth, high innovation market where the IT platforms are going through a re-architecture that you actually are public going through that growth phase. Now it forces discipline around how you operationalize the business and how you run the business, but I think that's very healthy for both the tech and the company. >> Michael Dell told me he wanted to go private mainly because he had to do some work essentially behind the curtain. Didn't want the 90-day shot clock, the demands of Wall Street. Other companies do it because the can't stand alone. They don't have a platform and they're constantly pivoting internally to try to grope and find that groove swing, if you will. You're saying that you guys have your groove swing and as Dave Velanti always says, always get behind a growing total adjustment market or TAM, you saying that. Okay, I buy that. So the TAM's growing. What are you guys doing on the platform side that's enabling your customers to re-platform and take advantage of their current data situation as well as the upcoming IoT boom that's being forecasted? >> Well, the first thing is the genesis of which we started the company around, which is we transformed Hadoop from being a batch architecture, single data set, single application, to being able to actually manage a central data architecture where all data comes under management and be able to drive and evolve from batch to batch interactive and real-time simultaneously over that central data set. And then making sure that it's truly an enterprise viable, enterprise ready platform to manage mission critical workloads at scale. And those are the areas where we're continuing to innovate around security, around data governance, around life cycle management, the operations and the management consoles. But then we want to expand the markets that we operate in and be world class and best tech on planet Earth for that data at rest and our core Hadoop business. But as we then see the opportunities to go out to the edge and from the point of origination truly manage and bring that data under management through its entire life cycle, through the movement process and create value. And so we want to continue to extend the reach of when we have data under management and the value we bring to the data through its entire life cycle. And then what's next is you have that data in its life cycle. You then move into the modern data applications, and if you look at what we've done with cyber security and some of the offerings that we've engaged in the cyber security space, that was our first entry. And that's proven to be a significant game changer for us and our customers both. >> Cyber security certainly a big data problem. Also a cloud opportunity with the horsepower you can get with computing. Give us the update. What are you seeing there from a traction standpoint? What's some of the level of engagements your having with enterprises outside of the NSA and the big government stuff, which I'm sure they're customers don't have to disclose that, but for the most part a normal enterprise are constantly planning as if they are already attacked and they're having different schemes that they're deploying. How are they using your platform for that right now? >> Well, the nature of attacks has changed. And it's evolved from just trying to find the hole in the firewall or where we get into the gateway, to how we find a way through a back door and just hang out in your network and watch for patterns and watch for the ability to aggregate relationships and then pose as a known entity that you can then cascade in. And in the world of cyber security you have to be able to understand those anomalies and be able to detect those anomalies that sit there and watch for their patterns to change. And as you go through a whole life cycle of data management between a cloud on Prim and a hybrid architecture, it opens up many, many opportunities for the bad guys to get in and have very new schemes. And our cyber security models give the ability to really track how those anomalies are attaching, where the patterns are emerging, and to be able to detect that in real-time and we're seeing the major enterprises shift to these new models, and it's become a very big part of our growth. >> So I got to change gears and ask you about open-source. You've been an open-source really from the beginning, I would call first generation commercial. But it was not a tier one citizen at that time. It was an alternative to other privatery platforms, whether you look at the network stack or certainly from software. Now today it's tier one. Still we hear business people kind of like, well, open-source. Why should a business executive care about opens-source now? And what would you say to that person who's watching about the benefits of open-source and some of the new models that could help them. >> Well, open-source in general's going to give a number of things. One, it's going to probably provide the best tech, the most innovation in a space, whether that be at the network layer or whether that be at the middle wear layer, the tools layer or certainly the data layer. And you're going to see more innovation typically happen on those platforms much faster and you've got transparent visibility into it. And it brings an ecosystem with it and I think that's really one of the fundamental issues that someone should be concerned with is what does the ecosystem around my tech look like? An open-source really draws forward a very big ecosystem in terms of innovators of the tech, but also enablers of the tech and adopters of the tech in terms of incremental applications, incremental tool sets. And what it does and the benefit to the end customer is the best tech, the most innovation, and typically operating models that don't generate lock in for 'em, and it gives them optionality to use the tech in the most appropriate architecture in the best economic model without being locked in to a proprietary path that they end up with no optionality. >> So talk about the do-it-yourself mentality. In IT that's always been frowned upon because it's been expensive, time-consuming, yet now with organic open-source and now with cloud, you saw that first generation do-it-yourself, standing up stuff on Amazon, whatnot, is being very viable. It funded shadow IT and a variety of other great things around virtualization, visualization, and so on. Today we're seeing that same pattern swing back to do-it-yourself, is good for organic innovation but causes some complexities. So I want to get your thoughts on this because this seems to be a common thread on our Cube interviews and at Hadoop Summit and at Big Data SV as part of Big Data Week when we were in town. We heard from customers and we heard the following: It's still complex and the total cost of ownership's still too high. That seems to be the common theme for slowing down the rapid acceleration of Hadoop and its ecosystem in general. One, do you agree with that? And two, if so, or what would be than answer to make that go faster? >> Well, I think you're seeing it accelerate. I think you're seeing the complexities dwindle away through both innovation and the tech and the maturing of the tech, as well as just new tool sets and applications that are leveraging it, that take away any complexity that was there. But what I think has been acknowledged is, the value that it creates and that it's worth the do-it-yourself and bringing together the spare techs because the innovation that it brings, the new architectures and the value that it creates as these platforms move into the different use cases that they're enabling. >> So I got to ask you this question. I know you're not going to like it and all the people always say, well John, why does everyone always ask that same question? You guys have a radically different approach than Cloudera. It's the number one question. I get ask them about Cloudera. Cloudera, ask them about Hortonworks. You guys have been battling. They were first. You guys came right fast followers second. With the Yahoo! thing we've been following you guys since day one. Explain the difference between Cloudera, because now a couple things have changed over the past few years. One is, Hadoop wasn't the be all end all for big data. There's been a lot of other things certainly SPARK and some other stuff happening, but yet now enterprises are adopting and coexisting with other stuff. So we've seen Cloudera make some pivots. They certainly got some good technology, but they've had some good right answers and some wrong answers. How've you guys been managing it because you're now public, so we can see all the numbers. We know what the business is doing. But relative to the industry, how are you guys compared to Cloudera? What's the differences? And what are you guys doing differently that makes Hortonworks a better vendor than Cloudera? >> I can't speak to all the Cloudera models and strategies. What I'll tell you is the foundation of our model and strategy is based on. When we founded the company we were as you mentioned, three of four years post Cloudera's founding. We felt like we needed to evolve Hadoop in terms of the architecture, and we didn't want to adopt the batch-oriented architecture. Instead we took the core Hadoop platform and through YARN enabled it to bring a central data architecture together as well as be able to be generating batch interactive in real-time applications, leveraging YARN as the data operating system for Hadoop. And then the real strategy behind that was to open up the data sets, open up the different types of use cases, be able to do it on a central data architecture. But then as other processing engines emerged, whether it be a SPARK as you brought up or some of the other ones that we see coming down the pipe, we can then integrate those engines through YARN onto the central data platform. And we open up the number of opportunities, and that's the core basis. I think that's different than some of the other competitor's technology architecture. >> Looking back now five years, are there moves that you were going to make that others have made, that you look back and say I'm glad we didn't do that given today's landscape? >> What I'm glad we did do is open up to the most use cases and workloads and data sets as possible through YARN, and that's proven to be a very, very, fundamentally differentiation of our model and strategy for anybody in the Hadoop space certainly. And I'm also very happy that we saw the opportunity about a year ago that it needed to be more than just about data at rest on Hadoop, and that actually to truly be the next generation data architecture, that you've got to be able to provide the platforms for data at rest and data in motion and our acquisition of Onyara, to be able to get the NiFi technology so that we're truly capturing the data from the point of origination all the way through the movement cycle until it comes at rest has given us now the ability to do a complete life cycle management for an entire data supply chain. And those decisions have proven to be very, very differentiation between us and any of our other competitors and it's opened up some very, very big markets. More importantly, it's accelerated the time to value that our customers get in the use cases that they're enabling through us. >> How would you talk about the scenario that people are saying about Hadoop not being the end all be all industry? At the same time, 'cause big data, as Aroon Merkey said on the Keblan Dublin. It's bigger than Hadoop now, but Hadoop has become synonymous with big data generally. Where's the leadership coming from in your mind? Because we're certainly not seeing it on the data warehouse side, 'cause those guys still have the old technology, trying to co-exist and re=platform for the future. So question is, is Hortonworks viewing Hadoop as still leading generically as a big data industry or has it become a sidebar of the big data industry? >> Of Hadoop? Hadoop is the platform, and we believe ground zero for big data. But we believe it's bigger than that. It's about all data and being able to manage the entire life cycle of all data, and that starts from the point of origination, until it comes at rest, and be able to continue to drive that entire life cycle. Hadoop certainly is the underpinning of the platform for big data, but it's really got to be about all data. Data at rest, data in motion, and what you'll see is the next leg in this is, the modern data applications that then emerge from that. >> How has the ecosystem in the Hadoop industry, I would agree with by the way the Hadoop players are leading big data in general in terms of innovation. The ecosystem's been a big part of it. You guys have invested in it. Certainly a lot of developers and open-source. How has the ecosystem changed given the current situation from where it was? And where do you see the ecosystem going? With the re-platforming not everyone can have a platform. There's a ton of guys out there that have tools, that are looking for a home, they're trying to figure out the chessboard on what's going on with the ecosystem. What's your thoughts of the current situation and how it will evolve in your view? >> Well, I think one of the strongest statements from day one is whether it's EDW or BI or relational, none of the traditional platform players say the way you solve your big data problem is with my platform. They to a company have a Hadoop platform strategy of some form to bring all of that huge volume of big data under management, and it fits our model very well in that we're not trying to disintermediate, but extend those platforms by leveraging HDP as an extension of their platform. And what that's done is it's created pool markets. It's brought Hadoop into the enterprise with a very specific value proposition in use case, bringing more data under management for that tool, that application, or that platform. And then the enterprises has realized there's other opportunities beyond that. And new use cases and new data sets, we can also gain more leverage from. And that's what's really accelerated-- >> So you see growth in the ecosystem? >> We're actually seeing exponential acceleration of the growth around the ecosystem. Not only in terms of the existing platform and tools and applications for either adopting Hadoop, but now new start-up companies building completely from scratch applications just for the big data sets. >> Let's talk about STARS. We were talking before we sat down about the challenges being an entrepreneur. You mentioned the exponential acceleration of entrepreneurs coming into the ecosystem. That's a safe harbor right now. It seems to be across the board. And a lot of the big platforms have robust, growing ecosystems. What's the current landscape of STARS? I know you're an active investor yourself and you're involved in a lot of different start-up conversations and advisor. What's your view of the current landscape right now? Series A, B, C, growth. Stalling. What needs to be in place for these companies to be successful? What are some of the things that you're seeing? >> You have to be surgically focused right now or on a very particular problem set, maybe even by industry. And understand how to solve the problem and have an absolute correlation to a value proposition and a very well defined and clear model of how you're going to go solve that problem, monetize it, and scale. Or you have to have an incredibly well-financed and deep war chest to go after a platform play that's going after a very large TAM that is enabling a re-platforming at one of the levels and the new IT landscape. >> So laser focus in a stack or vertical, and/or a huge cash from funded benchmark or other VCs, tier one VCs, to have a differentiator. They have to have some sort of enabler. >> To enable a next generation platform and something that's very transformational as a platform that really evolves the IT stack. >> What strategies would you advise entrepreneurs in terms of either white spaces to attack and/or their orientation to this new data layer? Because if this plays out as we were talking about, you're going to have a horizontal data layer where you need eye dropper ability. Need to have data in motion, but data aware. Smart data you integrate into disparate systems. Breaking down the siloed concept. How should an entrepreneur develop or look at that? Is there a certain model you've seen work successfully? Is there a certain open-source group they can jump into? What thoughts would you share? 'Cause this seems to be the toughest nut to crack for entrepreneurs. >> Right now you're seeing a massive shift in the IT data architecture, is one example. You're seeing another massive shift in the network architecture. For example, the SDN, right? You're seeing I think a big shift in the kinds of applications getting away from application functionality to data enabled applications. And I think it's important for the entrepreneur to understand where in the landscape do they really want to position? Where do they bring intellectual capital that can be monetized? Some of the areas that I think you'll see emerge very quickly in the next four, six, eight quarters are the new optimization engines, and so things around AI and machine learning. And now that we have all of the data under management through its entire life cycle, how do I now optimize both where that data's processed, in the cloud or on Prim, or as it's in motion. And there's a massive opportunity through software defined networking to actually come in and now optimize at the purest price point and/or efficiency where that data's managed, where that data's stored, and let it continue to reap the benefits. Just as Amazon's done in retail, if you like this, you should look at that. Just as Yahoo! did, I'll point out with Hadoop, it's advertising models and strategies of being able to put specific content in front of you. Those kinds of opportunities are now available for the processing and storage of data through the entire life cycle across any architectural strategy. >> Are you seeing data from a developer's standpoint being instrumental in their use cases? Meaning as I'm developing on top a data platforms like Hortonworks or others, where there's disparate data, what's their interaction? What's their relationship to the data? How are they using it? What do they need to know? Where's the line in terms of their involvement in the data? >> Well, what we're seeing is very big movement with the developed community that they now want to be able to just let the data tell them where the application service needs to be. Because in the new world of data they understand what the entity relationships are with their customers and the patterns that their customers happening. They now can highly optimize when their customers are about to cross over into from one event to the other, and what that typically means and therefore what the inverted action should be to create the best experience with their customer, to create a higher level of service, to be able to create a better packaged price point at a better margin. They also have the ability to understand it in real-time based on what the data trend is flowing, how well their product's performing. Any obstacles or issues that are happening with their product. So they don't want to have to have application logic that then they run a report on three days, three weeks after some events happened. They now are taking the data and as that data and events are happening in the data and it's telling them what to do and they're able to prescriptively act on whatever event or circumstance unfold from that. >> So they want the data now. They want real-time data embedded in the apps as on the front line developer. >> And they want to optimize what that data is doing as it's unfolding through its natural life cycle. >> Let's talk with your customer base and what their expectations are. What questions should a customer or potential customer ask to their big data vendor as they look at the future? What are the key questions they should ask? >> They should really be comparing what is your architectural strategy, first and foremost. For managing data. And what kinds of data can I manage? What are the limitations in your architecture? What workloads and data sets can't I manage? What are the latency issues that your architecture would create for me? What's your business model that's associated with us engaging together? How much of the life cycle can you enable of my data? How secure are you making my data? What kind of long tail of visibility and chain of custody can I have around the governance? What kind of governance standards are you applying to the data? How much of my governance standards can you help me automate? How easy is it to operate and how intuitive is it? How big is your ecosystem? What's your road map and your strategy? What's next in your application stack? >> So enterprises are looking at simplicity. They're looking for total cost of ownership. How is big data innovation going to solve that problem? Because with IoT, again, a lot of new stuff's happening really, really fast. How do they get their arms around this simplicity question in this total cost of ownership? How should they be thinking about it? >> Well, what the Hadoop platforms have to do and the data in motion platforms have to do is to be able to bring the data under management and bring all of the enterprise services that they have in their existing data platforms, in the areas of security, in the areas of management, in the areas of data governance, so they can truly run mission critical workloads at scale with all the same levels of predictability that they have in isolation, in their existing proprietary platforms. And be able to do it in a way that's very intuitive for their existing platforms to be able to access it, very intuitive for their operations teams to be able to manage it, and very clean and easy for their existing tools and platforms investments to leverage it. >> On the industry landscape right now what are you seeing if a consolidation? Some are saying we're seeing some consolidation. Lot of companies going private. You're seeing people buckle down. It's almost a line. If you weren't born before a certain date for the company, you might have the wrong architecture. Certainly enterprises re-platform, I would agree with that, but as a supplier to customers, you're one of the young guys. You were born in the cloud. You were born in open-source, Hortonworks. Not everyone else is like that, and certainly Oracle's like one of the big guys that keep on doing well. IBM's been around. But they're all changing, as well. And certainly a lot of these growth companies pre-IPO are kind of being sold off. What's your take on the current situation with the bubble, the softening, whatever people calling it. What's your thoughts? >> I think you see some companies who got caught up and if we sort of unpack that to the ones who are going private now, those are the companies that have operated in a very mature market space. They were able to not innovate as much as they would probably have liked to, they're probably locked into a proprietary technology in a non-subscription model of some sort. Maybe a perpetual license model. And those are very different models than the enterprise wants to adopt today and their ability to innovate and grow because the market shrank, forced them to go into very constrained environments. And ultimately, they can be great companies. They have great value propositions, but they need to go through transformations that don't include a 90-day shot clock in the public market. In the markets where there's maybe, I was in the B round or the C round and I was focused on providing a niche offering into one of those mature spaces that's becoming disintermediated or evolve quickly because an open-source company has come into the space or that section of IT stack has morphed into more of a cloud-centric or SAP-centric or an open-source centric environment. They got cut short. Their market's gone away. Their market shrunk. They can't innovate their way out of it. And they then ultimately have to find a different approach, and they may or may not be able to get the financing to do that. We're in a much different position. >> Certainly the down round. We're seeing down rounds from the high valuations. That's the first sign of trouble. >> That's the first sign. I've gotten three calls this week from companies that are liquidating and have two weeks to find a new home. >> Great, we'll look for some furniture for our new growing SiliconANGLE office. >> I think you'll have some good values. >> You personally, looking back over five year now in this journey, what an incredible run you guys have had and fun to watch you guys. What's the biggest thing that surprised you and what's the biggest thing that's happened? If you can talk about those two things 'cause again, a lots happened. The markets changed significantly. You guys went public. You got a big office here. What surprised you and what was the biggest thing that you think was the catalyst of the current trajectory? >> How quickly the market grew. We saw from day one when we started the company that this was a billion dollar opportunity, and that was the bar for starting whatever we did. We were looking for new opportunities. We had to see a billion dollar opportunity. How quickly we have seen the growth and the formation of the market in general. And then how quickly some of the new opportunities have opened up, in particular around streaming, Internet of Things, the new paradigm data sets, and how quickly the enterprises have seen the ability to create a next generation data architecture and the aggressiveness in which their moving to do that with Hadoop. And then how quickly in the last year it swung to also being able to want to bring data in motion under management, as well. >> If you could talk to a customer right here, right now, and they asked you the following question, Rob, look around the corner five years out. Tell me something that someone else can't see that you see, that I should be aware of in my business. And why should I go with Hortonworks? >> It's going to be a table stake requirement to be able to understand from whether it be your customer or your supply chain from the point they begin to engage and the first step towards engaging with your product or your service, what they're trying to accomplish, and to be able to interact with them from that first inception point. It's also going to be table stakes to understand to be able to monitor your product in real-time, and be able to understand how well it's performing, down to the component level so that you can make real-time corrections, improvements, and be able to do that on the fly. The other thing that you're going to see is that it's going to be a table stake requirement to be able to aggregate the data that's happened in that life cycle and give your customer the ability to monetize the data about them. But you as the enterprise will be responsible for creating anonymity, confidentiality and security of the data. But you're going to have to be able to provide the data about your customers and give them the ability to if they choose to monetize the data about them, that the ability to do so. >> So I get that correct, you're basically saying 100% digital. >> Oh, it's by far, within the next five years, absolutely. If you do not have a full digital model, in most industries you'll be disintermediated. >> Final question. What's the big bet that you're making right now at Hortonworks? That you say we're pinning the company on blank, fill in the blank. >> It's not about big data. It's about all data under management. >> Rob, thanks so much for spending the time here On the Ground. Rob Bearden, CEO of Hortonworks here for an executive On the Ground. I'm John for The Cube. Thanks for watching. (techno music)