>> Announcer: Live, from Orlando, Florida, it's The Cube. Covering PentahoWorld 2017. Brought to you by, Hitachi Vantara. >> Welcome back to Orlando, everybody. This is PentahoWorld, #pworld17 and this is The Cube, The leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Jim Kobielus Donna Prlich is here, she's the Chief Product Officer of Pentaho and a many-time Cube guest. Great to see you again. >> Thanks for coming on. >> No problem, happy to be here. >> So, I'm thrilled that you guys decided to re-initiate this event. You took a year off, but we were here in 2015 and learned a lot about Pentaho and especially about your customers and how they're applying this, sort of, end-to-end data pipeline platform that you guys have developed over a decade plus, but it was right after the acquisition by Hitachi. Let's start there, how has that gone? So they brought you in, kind of left you alone for awhile, but what's going on, bring us up to date. >> Yeah, so it's funny because it was 2015, it was PentahoWorld, second one, and we were like, wow, we're part of this new company, which is great, so for the first year we were really just driving against our core. Big-Data Integration, analytics business, and capturing a lot of that early big-data market. Then, probably in the last six months, with the initiation of Hitachi Ventara which really is less about Pentaho being merged into a company, and I think Brian covered it in a keynote, we're going to become a brand new entity, which Hitachi Vantara is now a new company, focused around software. So, obviously, they acquired us for all that big-data orchestration and analytics capability and so now, as part of that bigger organization, we're really at the center of that in terms of moving from edge to outcome, as Brian talked about, and how we focus on data, digital transformation and then achieving the outcome. So that's where we're at right now, which is exciting. So now we're part of this bigger portfolio of products that we have access to in some ways. >> Jim: And I should point out that Dave called you The CPO of Pentaho, but in fact you're the CPO of Hitachi Vantara, is that correct? >> No, so I am not. I am the CPO for the Pentaho product line, so it's a good point, though, because Pentaho brand, the product brand, stays the same. Because obviously we have 1,800 customers and a whole bunch of them are all around here. So I cover that product line for Hitachi Vantara. >> David: And there's a diverse set of products in the portfolios >> Yes. >> So I'm actually not sure if it makes sense to have a Chief Products officer for Hitachi Vantara, right? Maybe for different divisions it makes sense, right? But I've got to ask you, before the acquisition, how much were you guys thinking about IOT and Industrial IOT? It must have been on your mind, at about 2015 it certainly was a discussion point and GE was pushing all this stuff out there with the ads and things like that, but, how much was Pentaho thinking about it and how has that accelerated since the acquisition? >> At that time in my role, I had product marketing I think I had just taken Product Management and what we were seeing was all of these customers that were starting to leverage machine-generated data and were were thinking, well, this is IOT. And I remember going to a couple of our friendly analyst folks and they were like, yeah, that's IOT, so it was interesting, it was right before we were acquired. So, we'd always focus on these blueprints of we've got to find the repeatable patterns, whether it's Customer 360 in big data and we said, well they're is some kind of emerging pattern here of people leveraging sensor data to get a 360 of something. Whether it's a customer or a ship at sea. So, we started looking at that and going, we should start going after this opportunity and, in fact, some of the customers we've had for a long time, like IMS, who spoke today all around the connected cars. They were one of the early ones and then in the last year we've probably seen more than 100% growth in customers, purely from a Pentaho perspective, leveraging Machine-generated data with some other type of data for context to see the outcome. So, we were seeing it then, and then when we were acquired it was kind of like, oh this is cool now we're part of this bigger company that's going after IOT. So, absolutely, we were looking at it and starting to see those early use cases. >> Jim: A decade or more ago, Pentaho, at that time, became very much a pioneer in open-source analytics, you incorporated WECA, the open-source code base for machine-learning, data mining of sorts. Into the core of you're platform, today, here, at the conference you've announced Pentaho 8.0, which from what I can see is an interesting release because it brings stronger integration with the way the open-source analytic stack has evolved, there's some Spark Streaming integration, there's some Kafaka, some Hadoop and so forth. Can you give us a sense of what are the main points of 8.0, the differentiators for that release, and how it relates to where Pentaho has been and where you're going as a product group within Hiatachi Vantara. >> So, starting with where we've been and where we're going, as you said, Anthony DeShazor, Head of Customer Success, said today, 13 years, on Friday, that Pentaho started with a bunch of guys who were like, hey, we can figure out this BI thing and solve all the data problems and deliver the analytics in an open-source environment. So that's absolutely where we came form. Obviously over the years with big data emerging, we focused heavily on the big data integration and delivering the analytics. So, with 8.0, it's a perfect spot for us to be in because we look at IOT and the amount of data that's being generated and then need to address streaming data, data that's moving faster. This is a great way for us to pull in a lot of the capabilities needed to go after those types of opportunities and solve those types of challenges. The first one is really all about how can we connect better to streaming data. And as you mentioned, it's Spark Streaming, it's connecting to Kafka streams, it's connecting to the Knox gateway, all things that are about streaming data and then in the scale-up, scale-out kind of, how do we better maximize the processing resources, we announced in 7.1, I think we talked to you guys about it, the Adaptive Execution Layers, the idea that you could choose execution engine you want based on the processing you need. So you can choose the PDI engine, you can choose Spark. Hopefully over time we're going to see other engines emerge. So we made that easier, we added Horton Work Support to that and then this concept of, so that's to scale up, but then when you think about the scale-out, sometimes you want to be able to distribute the processing across your nodes and maybe you run out of capacity in a Pentaho server, you can add nodes now and then you can kind-of get rid of that capacity. So this concept of worker-nodes, and to your point earlier about the Hitachi Portfolio, we use some of the services in the foundry layer that Hitachi's been building as a platform. >> David: As a low balancer, right? >> As part of that, yes. So we could leverage what they had done which if you think about Hitachi, they're really good at storage, and a lot of things Pentaho doesn't have experience in, and infrastructure. So we said, well why are we trying to do this, why don't we see what these guys are doing and we leverage that as part of the Pentaho platform. So that's the first time we brought some of their technology into the mix with the Pentaho platform and I think we're going to see more of that and then, lastly, around the visual data prep, so how can we keep building on that experience to make data prep faster and easier. >> So can I ask you a really Columbo question on that sort-of load-balancing capabilities that you just described. >> That's a nice looking trench coat you're wearing. >> (laughter) gimme a little cigar. So, is that the equivalent of a resource negotiator? Do I think of that as sort of your own yarn? >> Donna: I knew you were going to ask me about that (laughter) >> Is that unfair to position it that way? >> It's a little bit different, conceptually, right, it's going to help you to better manage resources, but, if you think about Mesos and some of the capabilities that are out there that folks are using to do that, that's what we're leveraging, so it's really more about sometimes I just need more capacity for the Pentaho server, but I don't need it all the time. Not every customer is going to get to the scale that they need that so it's a really easy way to just keep bringing in as much capacity as you need and have it available. >> David: I see, so really efficient, sort of low-level kind of stuff. >> Yes. >> So, when you talk about distributed load execution, you're pushing more and more of the processing to the edge and, of course, Brian gave a great talk about edge to outcome. You and I were on a panel with Mark Hall and Ella Hilal about the, so called, "power of three" and you did a really good blog post on that the power of the IOT, and big data, and the third is either predictive analytics or machine learning, can you give us a quick sense for our viewers about what you mean by the power of three and how it relates to pushing more workloads to the edge and where Hitachi Vantara is going in terms of your roadmap in that direction for customers. >> Well, its interesting because one of the things we, maybe we have a recording of it, but kind of shrink down that conversation because it was a great conversation but we covered a lot of ground. Essentially that power of three is. We started with big data, so as we could capture more data we could store it, that gave us the ability to train and tune models much easier than we could before because it was always a challenge of, how do I have that much data to get my model more accurate. Then, over time everybody's become a data scientist with the emergence of R and it's kind of becoming a little bit easier for people to take advantage of those kinds of tools, so we saw more of that, and then you think about IOT, IOT is now generating even more data, so, as you said, you're not going to be able to process all of that, bring all that in and store it, it's not really efficient. So that's kind of creating this, we might need the machine learning there, at the edge. We definitely need it in that data store to keep it training and tuning those models, and so what it does is, though, is if you think about IMS, is they've captured all that data, they can use the predictive algorithms to do some of the associations between customer information and the censor data about driving habits, bring that together and so it's sort of this perfect storm of the amount of data that's coming in from IOT, the availability of the machine learning, and the data is really what's driving all of that, and I think that Mark Hall, on our panel, who's a really well-known data-mining expert was like, yeah, it all started because we had enough data to be able to do it. >> So I want to ask you, again, a product and maybe philosophy question. We've talked on the Cube a lot about the cornucopia of tooling that's out there and people who try to roll their own and. The big internet companies and the big banks, they get the resources to do it but they need companies like you. When we talk to your customers, they love the fact that there's an integrated data pipeline and you've made their lives simple. I think in 8.0 I saw spark, you're probably replacing MapReduce and making life simpler so you've curated a lot of these tools, but at the same time, you don't own you're own cloud, you're own database, et cetera. So, what's the philosophy of how you future-proof your platform when you know that there are new projects in Apache and new tooling coming out there. What's the secret sauce behind that? >> Well the first one is the open-source core because that just gave us the ability to have APIs, to extend, to build plugins, all of that in a community that does quite a bit of that, in fact, Kafka started with a customer that built a step, initially, we've now brought that into a product and created it as part of the platform but those are the things that in early market, a customer can do at first. We can see what emerges around that and then go. We will offer it to our customers as a step but we can also say, okay, now we're ready to productize this. So that's the first thing, and then I think the second one is really around when you see something like Spark emerge and we were all so focused on MapReduce and how are we going to make it easier and let's create tools to do that and we did that but then it was like MapReduce is going to go away, well there's still a lot of MapReduce out there, we know that. So we can see then, that MapReduce is going to be here and, I think the numbers are around 50/50, you probably know better than I do where Spark is versus MapReduce. I might be off but. >> Jim: If we had George Gilbert, he'd know. >> (laughs) Maybe ask George, yeah it's about 50/50. So you can't just abandon that, 'cause there's MapReduce out there, so it was, what are we going to do? Well, what we did in the Hadoop Distro days is we created a adaptive, big data layer that said, let's abstract a layer so that when we have to support a new distribution of Hadoop, we don't have to go back to the drawing board. So, it was the same thing with the execution engines. Okay, let's build this adaptive execution layer so that we're prepared to deal with other types of engines. I can build the transformation once, execute it anywhere, so that kind of philosophy of stepping back if you have that open platform, you can do those kinds of things, You can create those layers to remove all of that complexity because if you try to one-off and take on each one of those technologies, whether it's Spark or Flink or whatever's coming, as a product, and a product management organization, and a company, that's really difficult. So the community helps a ton on that, too. >> Donna, when you talk to customers about. You gave a great talk on the roadmap today to give a glimpse of where you guys are headed, your basic philosophy, your architecture, what are they pushing you for? Where are they trying to take you or where are you trying to take them? (laughs) >> (laughs) Hopefully, a little bit of both, right? I think it's being able to take advantage of the kinds of technologies, like you mentioned, that are emerging when they need them, but they also want us to make sure that all of that is really enterprise-ready, you're making it solid. Because we know from history and big data, a lot of those technologies are early, somebody has to get their knees skinned and all that with the first one. So they're really counting on us to really make it solid and quality and take care of all of those intricacies of delivering it in a non-open-source way where you're making it a real commercial product, so I think that's one thing. Then the second piece that we're seeing a lot more of as part of Hitachi we've moved up into the enterprise we also need to think a lot more about monitoring, administration, security, all of the things that go at the base of a pipeline. So, that scenario where they want us to focus. The great thing is, as part of Hitachi Vantara now, those aren't areas that we always had a lot of expertise in but Hitachi does 'cause those are kind of infrastructure-type technologies, so I think the push to do that is really strong and now we'll actually be able to do more of it because we've got that access to the portfolio. >> I don't know if this is a fair question for you, but I'm going to ask it anyway, because you just talked about some of the things Hitachi brings and that you can leverage and it's obvious that a lot of the things that Pentaho brings to Hitachi, the family but one of the things that's not talked about a lot is go-to-market, Hitachi data systems, traditionally don't have a lot of expertise at going to market with developers as the first step, where in your world you start. Has Pentaho been able to bring that cultural aspect to the new entity. >> For us, even though we have the open-source world, that's less of the developer and more of an architect or a CIO or somebody who's looking at that. >> David: Early adopter or. >> More and more it's the Chief Data Officer and that type of a persona. I think that, now that we are a entity, a brand new entity, that's a software-oriented company, we're absolutely going to play a way bigger role in that, because we brought software to market for 13 years. I think we've had early wins, we've had places where we're able to help. In an account, for instance, if you're in the data center, if that's where Hitachi is, if you start to get that partnership and we can start to draw the lines from, okay, who are the people that are now looking at, what's the big data strategy, what's the IOT strategy, where's the CDO. That's where we've had a much better opportunity to get to bigger sales in the enterprise in those global accounts, so I think we'll see more of that. Also there's the whole transformation of Hitachi as well, so I think there'll be a need to have much more of that software experience and also, Hitachi's hired two new executives, one on the sales side from SAP, and one who's now my boss, Brad Surak from GE Digital, so I think there's a lot of good, strong leadership around the software side and, obviously, all of the expertise that the folks at Pentaho have. >> That's interesting, that Chief Data Officer role is emerging as a target for you, we were at an event on Tuesday in Boston, there were about 200 Chief Data Officers there and I think about 25% had a Robotic Process Automation Initiative going on, they didn't ask about IOT just this little piece of IOT and then, Jim, Data Scientists and that whole world is now your world, okay great. Donna Prlich, thanks very much for coming to the Cube. Always a pleasure to see you. >> Donna: Yeah, thank you. >> Okay, Dave Velonte for Jim Kobielus. Keep it right there everybody, this is the Cube. We're live from PentahoWorld 2017 hashtag P-World 17. Brought to you by Hitachi Vantara, we'll be right back. (upbeat techno)

>> Narrator: Recovery. DataWorks Summit Europe 2017. Brought to you by Hortonworks. >> Hello everyone, welcome to The Cube's special presentation here in Munich, Germany for DataWorks Summit 2017. This is the Hadoop Summit powered by Hortonworks. This is their event and again, shows the transition from the Hadoop world to the big data world. I'm John Furrier. My co-host Dave Vellante, good to see you Dave. We're back in the seats together, usually on different events, but now here together in Munich. Great beer, great scene here. Small European event for Hortonworks and the ecosystem but it's called DataWorks 2017. Strata Hadoop is calling themselves Strata and Data. They're starting to see the word Hadoop being sunsetted from these events, which is a big theme of this year. The transition from Hadoop being the branded category to Data. >> Well, you're certainly seeing that in a number of ways. The titles of these events. Well, first of all, I love being in Europe. These venues are great, right? They're so Euro, very clean and magnificent. But back to your point. You're seeing the Hadoop Summit now called the DataWorks Summit. You're seeing the Strata Plus Hadoop is now Strata Plus, I don't even know what it is. Right, it's not Hadoop driven anymore. You see it also in Cloudera's IPO. They're going to talk about Hadoop and Hadoop Distro. They're a Hadoop Distro vendor but they talked about being a data management company and John, I think we are entering the era, or well deep into the era of what I have been calling for the last couple of years, profitless prosperity. Really where you see the Cloudera IPO, as you know, they raised money from Intel, over $600 million at a $4.1 billion dollar valuation. The Wall Street Journal says they'll have a tough time getting a billion dollar valuation. For every dollar each of these companies spends, Hortonworks and Cloudera, they lose between $1.70 and $2.50, so we've always said at SiliconANGLE, Wiki Bond and The Cube that people are going to make money in big data or the practitioners of big data, and it's hard to find those guys, it's hard to see them but that's really what's happening is the industries are transforming and those are the guys that are putting money into their bottom line. Not so much for technology vendors. >> Great to unpack that but first of all, I want to just say congratulations to Wiki Bond for getting it right again. As usual Wiki Bond, ahead of the curve and being out there and getting it right because I think you nailed it and I think Wiki Bond saw this first of all the research firms, kind of, you know, pat ourselves on the back here, but the truth is that practitioners are making the money and I think you're going to see more of that. In fact, last night as I'm having a nice beer here in Germany, I just like to listen to the conversations in the bar area and a lot of conversations around, real conversations around, you know, doing deals, and you know, deployments. You know, you're hearing about HBase, you're hearing about clusters, you're hearing about service revenue, and I think this is the focus. Cloudera, I think, in a classic Silicon Valley way, their hubris was tempered by their lack of scale. I mean, they didn't really blow it out. I mean, now they do 200 million in revenue. Nothing to shake a stick at, they did a great job, but they're buying revenue and Hortonworks is as well. But the ecosystem is the factor, and this is the wildcard. I'm making a prediction. Profitless prosperity that you point out is right, but I think that it has longevity with these companies like Hortonworks and Cloudera and others, like MapR because the ecosystem's robust. If you factor in the ecosystem revenue that is enough rising tide in my opinion. The question is how do they become sustainable as a standalone venture, that Red Hat for Hadoop never worked as Pat Gilson, you know, predicted. So, I think you're going to see a quick shift and pivot quickly by Hortonworks, certainly Cloudera's going to be under the microscope once they go public. I'm expecting that valuation to plummet like a rock. They're going to go public, Silicon Valley people are going to get their exits but. >> Excel will be happy. >> Everyone, yeah, they'll be happy. They already sold in 2013. They did a big sale, I mean, all of them cashed out two years ago when that liquidation event happened with Intel but that's fine. But now it's back to business building and Hortonworks has been doing it for years, so when you see your evaluation is less than a billion, so I'm expecting Cloudera to plummet like a rock. I would not buy the IPO at all because I think it's going to go well under a billion dollars. >> And I think it's the right call and as we know, last year, at the end of last year, Fidelity and other mutual funds devalued their holdings in Cloudera and so, you know, you've got this situation where, as you say, a couple hundred, maybe you know, on the way to 300 million in revenue, Hortonworks on the way to 200 million in revenue. Add up the ecosystem, yeah, maybe you get to a billion, throw in all of what IBM and Oracle call big data, and it's kind of a more interesting business, but you've called it same wine, new bottle. Is it a new bottle? Now, what I mean by that is the shift from Hadoop and then again, you read Cloudera's S1, it's all about AI, machine learning, you know, the cloud. Interesting, we'll talk about the cloud a little later, but is it same wine, new bottle, or is this really a shift toward a new era of innovation? >> It's not a new shift. It's the same innovation that the Hortonworks was founded on. Big data is a categorical and Hadoop was the horse they rode in on, but I think what's changing is the fact that customers are now putting real projects on the table and the scrutiny around those projects have to produce value, and the value comes down to total cost of ownership and business value. And that's becoming a data specific thing, and you look at all the successes in the big data world, Spark and others, you're seeing a focus on cloud integration and real-time workloads. These are real projects. This isn't fantasy. This isn't hype. This isn't early adopter. These are real companies saying we are moving to a new paradigm of digital transforming our companies and we need cost efficiencies but revenue-producing applications and workloads that are going to be running in the cloud with data at the heart of it. So, this is a customer-forcing function where the customers are generally excited about machine learning, moving to real-time classification of workloads. This is the deal and no hubris, no technology posturing, no open standards, jockeying can right the situation. Customers have demands and they want them filled, and we're going to have a lot of guests on here and I'm going to ask them those direct questions. What are you looking for and? >> Well, I totally agree with what you're saying and when we first met, it was right around the, you know, the mid point of the web 2.0 era, and I remember Tim Berners-Lee commenting on all this excitement, everybody's doing, he said this is what the web was invented to do, and this is what big data was invented to do. It was to produce deep analytics, deep learning, machine learning, you know, cognitive, as IBM likes to brand that, and so, it really is the next era even though people don't like to use the term big data anymore. We were talking to, you know, some of the folks in our community earlier, John, you and I, about some of the challenges. Why is it profitless, you know? Why is there so much growth but it's no profit? And you know, we have to point out here that people like Hortonworks and Cloudera, they've made some big bets, take HDSF of example. And now you have the cloud guys, particularly Amazon, coming in, you know, with S3. Look at YARN, big open source project. But you got Docker and Kubernetes seem to be mopping that up. Tez was supposed to replace MapReduce and now you've got. >> I mean, I wouldn't say mopping up, I mean. >> You've got Spark. >> At the end of the day the ecosystem's going to revolve around what the customers want, and portability of workloads, Kubernetes and microservices, these are areas that just absolutely make a lot of sense and I think, you know, people will move to where the frictionless action is and that's going to happen with Kubernetes and containers and microservices, but that just speaks to the devops culture, and I think Hadoop ecosystem, again, was grounded in the devops culture. So, yeah, there's some progress that are going to maybe go out of flavor, but there's other stuff coming up trough the ranks in open source and I think it's compelling. >> But where I disagree with what you're saying is well, the point I'm trying to make, is you have to, if you're Cloudera and Hortonworks, you have to support those multiple projects and it's expensive as hell. Whereas the cloud guys put all their wood behind one arrow, to use an old Scott McNealy phrase, and you know, Amazon, I would argue is mopping up in big data. I think the cloud guys, you know, it's ironic to me that Cloudera in the cloud era picked that name, you know, but really never had. >> John: They missed the cloud. >> They've never really had a strong cloud play, and I would say the same thing with Hortonworks and MapR. They have to play in the cloud and they talk about cloud, but they've got to support hybrid, they've got to support on param, they got to pick the clouds that they're going to support, AWS, Azure, maybe IBM's cloud. >> Look, Cloudera completely missed the cloud era, pun intended. However, they didn't miss open source but they're great at and I'm an admirer of Cloudera and Hortonworks on is that their open source ethos is what drove them, and so they kind of got isolated in with some of their product decisions, but that's not a bad thing. I mean, ultimately, I'm really bullish on Cloudera and Hortonworks because the ecosystem points I mentioned earlier are not high on the I wouldn't buy the IPO, I think I'd buy them at a discount, but Cloudera's not going to go away, Dave. They're going to go public. I think the valuation's going to drop like a rock and then settle around a billion, but they have good management. The founders still there, Michael Olson, Amr Awadallah. So, you're going to see Cloudera transform as a company. They have to do business out in the open and they're not afraid to, obviously they're open source. So, we're going to start to see that transition from a private venture backed, scale up, buy revenue. In the playbook of Silicon Valley venture capital's Excel partners and Greylock. Now they go public and get liquid and then now next phase of their journey is going to be build a public company and I think that they will do a good job doing it and I'm not down on them at all for that and I think it's just going to be a transition. >> Well, they're going to raise what? A couple 100 million dollars? But this industry, yeah, this industry's cashflow negative, so I agree with you. Open source is great, let's ra-ra for open source and it drives innovation, but how does this industry pay for itself? That's what I want to know. How you respond to that? >> Well, I think they have sustainable issues around services and I think partnering with the big companies like Intel that have professional services might help them on that front, but Michael Olson said in his founder's letter in his S1, kind of AI washing, he said AI and cognitive. But that's okay because Cloudera could easily pivot with their brain power, and same with Hortonworks to AI. Machine learning is very open source driven. Open source culture is growing, it's not going away, so I think Cloudera's in a very good position. >> I think the cloud guys are going to kill them in that game, and cloud guys and IBM are going to cream these profitless startups in that AI and machine learning game. >> We'll see. >> You disagree? >> I disagree, I think. Well, I mean, it depends. I mean, you know, I'm not going to, you know, forecast what the managements might do, but I mean, if I'm cloud looking at what Cloudera's done. >> What would you do? >> I would do exactly what Mike Olson's doing is I'd basically pivot immediately to machine learning. Look at Google. TensorFlow it's go so much traction with their cloud because it's got machine learning built into it. Open source is where the action is, and that's where you could do a lot of good work and use it as an advantage in that they know that game. I would not count out the open source game. >> So, we know how IBM makes money at that, you know, in theory anyway it wants. We know how Amazon's going to make money at that with their priority approach, Microsoft will do the same thing. How to Cloudera and Hortonworks make money? >> I think it's a product transition around getting to the open source with cloud technologies. Amazon is not out to kill open source, so I think there's an opportunity to wedge in a position there, and so they just got to move quickly. If they don't make these decisions then that's a failed execution on the management team at Cloudera and Hortonworks and I think they're on it. So, we'll keep an eye on that. >> No, Amazon's not trying to kill open source, I would agree, but they are bogarting open source in a big way and profiting amazingly from it. >> Well, they just do what Amy Jessie would say, they're customer driven. So, if a customer doesn't want to do five things to do one thing this is back to my point. The customers want real-time workloads. They want it with open source and they don't want all these steps in the cost of ownership. That's why this is not a new shift, it's the same wine, new bottle because now you're just seeing real projects that are demanding successful and efficient code and support and whoever delivers it builds the better mousetrap. In this case, the better mousetrap will win. >> And I'm arguing that the better mousetrap and the better marginal economics, I know I'm like a broken record on this, but if I take Kinesis and DynamoDB and Red Ship and wrap it into my big data play, offer it as a service with a set of APIs on the cloud, like AWS is going to do, or is doing, and Azure is doing, that's a better business model than, as you say, five different pieces that I have to cobble together. It's just not economically viable for customers to do that. >> Well, we've got some big new coming up here. We're going to have two days of wall-to-wall coverage of DataWorks 2017. Hortonworks announcing 2.6 of their Hadoop Hortonworks data platform. We're going to talk to Scott now, the CTO, coming up shortly. Stay with us for exclusive coverage of DataWorks in Munich, Germany 2017. We'll be back with more after this short break.