>> Announcer: Live from Orlando, Florida. It's TheCUBE covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, we are wrapping up day one. I'm your host Rebecca Knight along with my cohosts today James Kobielus and Dave Vellante. Guys, day one is done what have we learned? What's been the most exciting thing that you've seen at this conference? >> The most exciting thing is that clearly Hitachi Ventara which of course, Pentaho is a centerpiece is very much building on their strong background and legacy and open analytics, and pushing towards open analytics in the Internet of things, their portfolio, the whole edge to outcome theme, with Brian Householder doing a sensational Keynote this morning, laying out their strategic directions now Dave had a great conversation with him on TheCUBE earlier but I was very impressed with the fact that they've got a dynamic leader and a dynamic strategy, and just as important Hitachi, the parent company, has clearly put together three product units that make sense. You got strong data integration, you got a strong industrial IOT focus, and you got a really strong predictive and machine learning capability with Pentaho for the driving the entire pipeline towards the edge. Now that to me shows that they've got all the basic strategic components necessary to seize the future, further possibilities. Now, they brought a lot of really good customers on, including our latest one from IMS, Hillove, to discuss exactly what they're doing in that area. So I was impressed with the amount of solid substance of them seizing the opportunity. >> Well so I go back two years, when TheCUBE first did PentagoWorld 2015, and the story then was pretty strong. You had a company in big data, they seemingly were successful, they had a lot of good customer references, they achieved escape velocity, and had a nice exit under Quentin Galavine, who was the CEO at the time and the team. And they had a really really good story, I thought. But I was like okay, now what? We heard about conceptually we're going to bring the industrial internet and analytics together, and then it kind of got quiet for two years. And now, you're starting to see the strategy take shape in typical Hitachi form. They tend not to just rush in to big changes and transformations like this, they've been around for a long time, a very thoughtful company. I kind of look at Hitachi limited in a way, as an IBM like company of Japan, even though they do industrial equipment, and IBM's obviously in a somewhat different business, but they're very thoughtful. And so I like the story the problem I see is not enough people know about the story. Brian was very transparent this morning, how many people do business with Hitachi? Very few. And so I want to see the ecosystem grow. The ecosystem here is Hitachi, a couple of big data players, I don't see any reason why they can't explode this event and the ecosystem around Hitachi Ventara, to fulfill it's vision. I think that that's a key aspect of what they have to do. >> I want to see-- >> What will be the tipping point? Just to get as you said, I mean it's the brand awareness, and every customer we had on the show really said, when he when he said that my eyes lit up and I thought oh wow, we could actually be doing more stuff with Hitachi, there's more here. >> I want to see a strong developer focus, >> Yeah. >> Going forward, that focuses on AI and deep learning at the at the edge. I'm not hearing a lot of that here at PentahoWorld, of that rate now. So that to me is a strategic gap right now and what they're offering. When everybody across the IT and data and so forth is going real deep on things like frameworks like TensorFlow and so forth, for building evermore sophisticated, data driven algorithms with the full training pipeline and deployment and all that, I'm not hearing a lot of that from the Pentaho product group or from the Hitachi Ventara group here at this event. So next year at this event I would like to hear more of what they're doing in that area. For them to really succeed, they're going to have to have a solid strategy to migrate up there, openstack to include like I said, a bit of TensorFlow, MXNet, or some of the other deep learning tool kits that are becoming essentially defacto standards with developers. >> Yeah, so I mean I think the vision's right. Many of the pieces are in place, and the pieces that aren't there, I'm actually not that worried about, because Hitachi has the resources to go get them, either build them organically, which has proven it can do overtime, or bring in acquisition. Hitachi is a decent acquire of companies. Its content platform came in on an acquisition, I've seen them do some hardware acquisitions, some have worked, some haven't. But there's a lot of interesting software players out there and I think there's some values, frankly. The big data, tons of money poured in to this open source world, hard to make money in opensource, which means I think companies like Hitachi could pick off to do some M and A and find some value. Personally, I think if the numbers right at a half a billion dollars, I personally think that that was pretty good value for Hitachi. You see in all these multi billion dollar acquisitions going left and right. And so the other thing is the fact that Hitachi under the leadership under Brian Householder and others, was able to shift its model from 80% hardware, now it's 50/50 software and services I'd like to dig into that a little bit. They're a public company but you can't really peel the onion on the Hitachi Ventara side, so it kind of is what they say it is, I would imagine that's a lot of infrastructure software, kind of like EMC's a software company. >> James: Right. >> But nonetheless, they're moving towards a subscription model, they're committed to that, and I think that the other thing is that a lot of costumers. We come to a lot of shows and they struggle to get costumers on with substantive stories, so we heard virtually every costumer we talked to today is like Here's how I'm using Pentaho, here's how it's affecting. Not like super sexy stories yet, I mean that's what the IOT and the edge piece come in, but fundamental plumbing around big data, Pentaho seems like a pretty important piece of it. >> Their fundamental-- >> Their fundamental plumbing that's really saving them a lot of money too, and having a big ROI. >> They're fairly blue-chip as a solution provider of a full core data of a portfolio of Pentaho. I think of them in many ways as sort of like SAP, not a flashy vendor, but a very much a solid blue-chip in their core markets >> Right. >> I'm just naming another vendor that I don't see with a strong AI focus yet. >> Yeah. >> Pentaho, nothing to sneeze at when you have one customer after another like we've had here, rolling out some significant work they've been doing with Pantaho for quite a while, not to sneeze at their delivering value but they have to rise to the next level of value before long, to avoid be left in the dust. >> You got this data obviously they're going to be capturing more more data with the devices. >> James: Yeah. >> And The relationship with Hitachi proper, the elevator makers is still a little fuzzy to me, I'm trying to understand how that all shakes up, but my question for you Jim is: okay so let's assume for second they're going to have this infrastructure in place because they are industrial internet, and they got the analytics platform, maybe there's some holes that they can fill in, one being AI and some of the deep learning stuff, can't they get that somewhere? I mean there's so much action going on-- >> Yes. >> In the AI world, can't they bring that in and learn how to apply it overtime? >> Of course they can. First of all they can acquire and tap their own internal expertise. They've got like Mark Hall for example on the panel, they've obviously got a deep bench of data scientist like him who can take it to that next level, that's important. I think another thing that Hitachi Ventara needs to do to take it to the next level is they need a strong robotics portfolio. It's really talking about industrial internet of things, it's robotics with AI inside. I think they're definitely a company that could go there fairly quickly, a wide range of partners they can bring in or acquire to get fairly significant in terms of not just robotics in general, but robotics for a broad range of use cases where the AI is not so much the supervise learning and stuff that involves training, but things like reinforcement learning, and there's a fair amount of smarts and academe on Reinforcement learning for in body cognition, for robots, that's out there in terms of that's like the untapped space other than the broad AI portfolio, reinforcement learning. If somebody's going to innovate and differentiate themselves in terms of the enterprise, in terms of leveraging robotics in a variety of applications, it's going to to be somebody with a really strong grounding and reinforcement learning and productizing that and baking that in to an actual solution portfolio, I don't see yet the Google's and the IBM's and the Microsofts going there, and so if these guys want to stand out, that's one area they might explore. >> Yeah, and I think to pick up on that, I think this notion of robotics process automation, that market's going to explode. We were at a conference this week in Boston, the data rowdy of Boston, the chief data officer conference at the Park Plaza, 20 to 25% of the audiences, the CDO's in the audience had some kind of RPA, robotic process automation, initiative going on which I thought was astoundingly high. And so it would seem to me that Hitachi's going to be in a good position to capture all that data. The other thing that Brian stressed, which a lot of companies without a cloud will stress, is that it's your data, you own the data, we're not trying to resell that data, monetize that data, repackage that data. I pushed him a little bit on well what about that data training models, and where do those models go? And he says Look we are not in the business of taking models and you know as a big consultancy, and bringing it over to other competitors. Now Hitachi does have consultancy, but it's sort of in a focus, but as Brian said in his keynote, you have to listen to what people say and then watch them to see how they act. >> Rebecca: Do they walk the walk? >> How they respond. >> Right. >> And so that's you have to make your decision, but I do think that's going to be a very interesting field to watch because Hitachi's going to have so much data in their devices. Of course they're going to mine that data for things like predictive analytics, those devices are going to be in factories, they're going to be in ecosystems, and there's going to be a battle for who owns the data, and it's going to be really interesting to see how that shakes out. >> So I want to ask you both, as you've both have said, we've had a lot of great customer stories here on TheCUBE today. We had a woman who does autonomous vehicles, we had a gamer from Finland, we had a benefit scientist out of Massachusetts, Who were your favorite customer stories and what excited you most about their stories? >> James: Hmmm. >> Well I know you like the car woman. >> Well, yeah the car woman, >> The car woman. >> Ella Hillel. >> Ella Hillel, Yes. >> The PHD. That was really what I found many things fascinating, I was on a panel with Ella as well as she was on TheCUBE, what I found interesting I was expecting her to go to town on all things autonomous driving, self driving vehicles, and so forth, was she actually talked about the augmentation of the driver, passenger experience through analytics, dashboards in the sense that dashboards that help not only drivers but insurance companies and fleet managers, to do behavioral modification to help them modify the behavior, to get the most out of their vehicular experience, like reducing wear and tear on tires, and by taking better roads, or revising I thought that's kind of interesting; build more of the recommendation engine capability into the overall driving experience. That depends on an infrastructure of predictive analytics and big data, but also metered data coming from the vehicle and so forth. I found that really interesting because they're doing work clearly in that area, that's an area that you don't need levels one through five of self driving vehicles to get that. You can get that at any level of that whole model, just by bringing those analytics somehow into an organic way hopefully safely, into your current driving experience, maybe through a heads-up display that's integrated through your GPS or whatever might be, I found that interesting because that's something you could roll out universally, and it can actually make a huge difference in A: safety, B: people's sort of pleasure with the driving experience, Fahrvergnugen that's a Volkswagon, and then also see how people make the best use of their own vehicular assets in an era where people still mostly own their own car. >> Well for me if there's gambling involved-- >> Rebecca: You're there. >> It was the gaming, now not only because of the gambling, and we didn't find out how to beat the house Leonard, maybe next time, but it was confirmation of the three-tier data model from from edge-- >> James: Yes. >> To gateway to cloud, and that the cloud is two vectors; the on-premise and the off-premise cloud, and the fact that as a gaming company who designs their own slot machines it's an edge device, and they're basically instrumenting that edge device for real-time interactions. He said that most of the data will go back, I'm not sure. Maybe in that situation it might, maybe all the data will go back like weather data, it all comes back, But generally speaking I think there's going to be a lot of analog data at the edge that's going to be digitize that maybe you don't have to save and persist. But anyway, confirmation of that three-tiered data model I think is important because I think that is how Brian talked about it, we all know the pendulum is swinging, swung away from mainframe to decentralize back to the centralized data center and now it's swinging again to a much more distributed sort of data architecture. So it was good to hear confirmation of that, and I think it's again, it's really early innings in terms of how that all shakes out. >> Great, and we'll know more tomorrow at Pentaho day two, and I look forward to to being up here again with both of you tomorrow. >> Likewise. >> Great, this has been TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, I'm Rebecca Knight for Jim Kobielus and Dave Vellante, we'll see you back here tomorrow.

>> 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)