>> 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)

>> Announcer: Live from San Jose, California, it's theCUBE. Covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. Here live in Silicon Valley this is theCUBE. I'm John Furrier, covering our Big Data SV event, #BigDataSV. Our companion event to Big Data NYC, all in conjunction Strata Hadoop, the Big Data World comes together, and great to have guests come by. Donna Prlich, who's the senior VP of products and solutions at Pentaho, a Hitachi company who we've been following before Hitachi had acquired you guys. But you guys are unique in the sense that you're a company within Hitachi left alone after the acquisition. You're now running all the products. Congratulations, welcome back, great to see you. >> Yeah, thank you, good to be back. It's been a little while, but I think you've had some of our other friends on here, as well. >> Yep, and we'll be at Pentaho World, you have Orlando, I think is October. >> Yeah, October, so I'm excited about that, too, so. >> I'm sure the agenda is not yet baked for that because it's early in the year. But what's going on with Hitachi? Give us the update, because you're now, your purview into the product roadmap. The Big Data World, you guys have been very, very successful taking this approach to big data. It's been different and unique to others. >> [Donna} Yep. What's the update? >> Yeah, so, very exciting, actually. So, we've seen, especially at the show that the Big Data World, we all know that it's here. It's monetizable, it's where we, actually, where we shifted five years ago, and it's been a lot of what Pentaho's success has been based on. We're excited because the Hitachi acquisition, as you mentioned, sets us up for the next bit thing, which is IOT. And I've been hearing non-stop about machine learning, but that's the other component of it that's exciting for us. So, yeah, Hitachi, we're-- >> You guys doing a lot of machine learning, a lot of machine learning? >> So we, announced our own kind of own orchestration capabilities that really target how do you, it's less about building models, and how do you enable the data scientists and data preparers to leverage the actual kind of intellectual properties that companies have in those models they've built to transform their business. So we have our own, and then the other exciting piece on the Hitachi side is, on the products, we're now at the point where we're running as Pentaho, but we have access to these amazing labs, which there's about 25 to 50 depending on where you are, whether you're here or in Japan. And those data scientists are working on really interesting things on the R & D side, when you apply those to the kind of use cases we're solving for, that's just like a kid in a candy store with technology, so that's a great-- >> Yeah, you had a built-in customer there. But before I get into Pentaho focusing on what's unique, really happening within you guys with the product, especially with machine learning and AI, as it starts to really get some great momentum. But I want to get your take on what you see happening in the marketplace. Because you've seen the early days and as it's now, hitting a whole another step function as we approach machine learning and AI. Autonomous vehicles, sensors, everything's coming. How are enterprises in these new businesses, whether they're people supporting smart cities or a smart home or automotive, autonomous vehicles. What's the trends you are seeing that are really hitting the pavement here. >> Yeah, I think what we're seeing is, and it's been kind of Pentaho's focus for a long time now, which is it's always about the data. You know, what's the data challenge? Some of the amounts of data which everybody talks about from IOT, and then what's interesting is, it's not about kind of the concepts around AI that have been around forever, but when you start to apply some of those AI concepts to a data pipeline, for instance. We always talk about that 6data pipeline. The reason it's important is because you're really bringing together the data and the analytics. You can't separate those two things, and that's been kind of not only a Pentaho-specific, sort of bent that I've had for years, but a personal one, as well. That, hey, when you start separating it, it makes it really hard to get to any kind of value. So I think what we're doing, and what we're going to be seeing going forward, is applying AI to some of the things that, in a way, will close the gaps between the process and the people, and the data and the analytics that have been around for years. And we see those gaps closing with some of the tools that are emerging around preparing data. But really, when you start to bring some of that machine learning into that picture, and you start applying math to preparing data, that's where it gets really interesting. And I think we'll see some of that automation start to happen. >> So I got to ask you, what is unique about Pentaho? Take a minute to share with the audience some of the unique things that you guys are doing that's different in this sea of people trying to figure out big data. You guys are doing well, an6d you wrote a blog post that I referenced earlier yesterday, around these gaps. How, what's unique about Pentaho and what are you guys doing with examples that you could share? >> Yeah, so I think the big thing about Pentaho that's unique is that it's solving that analytics workflow from the data side. Always from the data. We've always believed that those two things go together. When you build a platform that's really flexible, it's based on open source technology, and you go into a world where a customer says, "I not only want to manage and have a data lake available," for instance, "I want to be able to have that thing extend over the years to support different groups of users. I don't want to deliver it to a tool, I want to deliver it to an application, I want to embed analytics." That's where having a complete end-to-end platform that can orchestrate the data and the analytics across the board is really unique. And what's happened is, it's like, the time has come. Where all we're hearing is, hey, I used to think it was throw some data over and, "here you go, here's the tools." The tools are really easy, so that's great. Now we have all kinds of people that can do analytics, but who's minding the data? With that end-to-end platform, we've always been able to solve for that. And when you move in the open source piece, that just makes it much easier when things like Spark emerge, right. Spark's amazing, right? But we know there's other things on the horizon. Flink, Beam, how are you going to deal with that without being kind of open source, so this is-- >> You guys made a good bet there, and your blog post got my attention because of the title. It wasn't click bait either, it was actually a great article, and I just shared it on Twitter. The Holy Grail of analytics is the value between data and insight. And this is interesting, it's about the data, it's in bold, data, data, data. Data's the hardest part. I get that. But I got to ask you, with cloud computing, you can see the trends of commoditization. You're renting stuff, and you got tools like Kinesis, Redshift on Amazon, and Azure's got tools, so you don't really own that, but the data, you own, right? >> Yeah, that's your intellectual property, right? >> But that's the heart of your piece here, isn't it, the Holy Grail. >> Yes, it is. >> What is that Holy Grail? >> Yeah, that Holy Grail is when you can bring those two things together. The analytics and the data, and you've got some governance, you've got the control. But you're allowing the access that lets the business derive value. For instance, we just had a customer, I think Eric might have mentioned it, but they're a really interesting customer. They're one of the largest community colleges in the country, Ivy Tech, and they won an award, actually, for their data excellence. But what's interesting about them is, they said we're going to create a data democracy. We want data to be available because we know that we see students dropping out, we can't be efficient, people can't get the data that they need, we have old school reporting. So they took Pentaho, and they really transformed the way they think about running their organization and their community colleges. Now they're adding predictive to that. So they've got this data democracy, but now they're looking at things like, "Okay we an see where certain classes are over capacity, but what if we could predict, next year, not only which classes are over capacity, what's the tendency of a particular student to drop out?" "What could we do to intervene?" That's where the kind of cool machine learning starts to apply. Well, Pentaho is what enables that data democracy across the board. I think that's where, when I look at it from a customer perspective, it's really kind of, it's only going to get more interesting. >> And with RFID and smart phones, you could have attendance tracking, too. You know, who's not showing up. >> Yeah absolutely. And you bring Hitachi into the picture, and you think about, for instance, from an IOT perspective, you might be capturing data from devices, and you've got a digital twin, right? And then you bring that data in with data that might be in a data lake, and you can set a threshold, and say, "Okay, not only do we want to be able to know where that student is," or whatever, "we want to trigger something back to that device," and say, "hey, here's a workshop for you to login to right away, so that you don't end up not passing a class." Or whatever it is, it's a simplistic model, but you can imagine where that starts to really become transformative. >> So I asked Eric a question yest6erday. It was from Dave Valante, who's in Boston, stuck in the snowstorm, but he was watching, and I'll ask you and see how it matches. He wrote it differently on Crouch, it was public, but this is in my chat, "HDS is known for main frames, historically, and storage, but Hitachi is an industrial giant. How is Pentaho leveraging the Hitachi monster?" >> Yes, that's a great way to put it. >> Or Godzilla, because it's Japan. >> We were just comparing notes. We were like, "Well, is it an $88 billion company or $90 billion. According to the yen today, it's 88. We usually say 90, but close enough, right? But yeah, it's a huge company. They're in every industry. Make all kinds of things. Pretty much, they've got the OT of the world under their belt. How we're leveraging it is number one, what that brings to the table, in terms of the transformations from a software perspective and data that we can bring to the table and the expertise. The other piece is, we've got a huge opportunity, via the Hitachi channel, which is what's seeing for us the growth that we've had over the last couple of years. It's been really significant since we were acquired. And then the next piece is how do we become part of that bigger Hitachi IOT strategy. And what's been starting to happen there is, as I mentioned before, you can kind of probably put the math together without giving anything away. But you think about capturing, being able to capture device data, being able to bring it into the digital twin, all of that. And then you think about, "Okay, and what if I added Pentaho to the mix?" That's pretty exciting. You bring those things together, and then you add a whole bunch of expertise and machine learning and you're like, okay. You could start to do, you could start to see where the IOT piece of it is where we're really going to-- >> IOT is a forcing function, would you agree? >> Yes, absolutely. >> It's really forcing IT to go, "Whoa, this is coming down fast." And AI and machine learning, and cloud, is just forcing everyone. >> Yeah, exactly. And when we came into the big data market, whatever it was, five years ago, in the early market it's always hard to kind of get in there. But one of the things that we were able to do, when it was sort of, people were still just talking about BI would say, "Have you heard about this stuff called big data, it's going to be hard." You are going to have to take advantage of this. And the same thing is happening with IOT. So the fact that we can be in these environments where customers are starting to see the value of the machine generated data, that's going to be-- >> And it's transformative for the business, like the community college example. >> Totally transformative, yeah. The other one was, I think Eric might have mentioned, the IMS, where all the sudden you're transforming the insurance industry. There's always looking at charts of, "I'm a 17-year-old kid," "Okay, you're rate should be this because you're a 17-year-old boy." And now they're starting to track the driving, and say, "Well, actually, maybe not, maybe you get a discount." >> Time for the self-driving car. >> Transforming, yeah. >> Well, Donna, I appreciate it. Give us a quick tease here, on Pentaho World coming in October. I know it's super early, but you have a roadmap on the product side, so you can see a little bit around the corner. >> Donna: Yeah. >> What is coming down the pike for Pentaho? What are the things that you guys are beavering away at inside the product group? >> Yeah, I think you're going to see some really cool innovations we're doing. I won't, on the Spark side, but with execution engines, in general, we're going to have some really interesting kind of innovative stuff coming. More on the machine learning coming out, and if you think about, if data is, you know what, is the hard part, just think about applying machine learning to the data, and I think you can think of some really cool things, we're going to come up with. >> We're going to need algorithms for the algorithms, machine learning for the machine learning, and, of course, humans to be smarter. Donna, thanks so much for sharing here inside theCUBE, appreciate it. >> Thank you. >> Pentaho, check them out. Going to be at Pentaho World in October, as well, in theCUBE, and hopefully we can get some more deep dives on, with their analyst group, for what's going on with the engines of innovation there. More CUBE coverage live from Silicon Valley for Big Data SV, in conjunction with Strata Hadoop, I'm John Furrier. Be right back with more after this short break. (techno music)

(upbeat music) >> Announcer: Live from Orlando Florida, it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's live coverage of PentahoWorld, brought to you of course by Hitachi Vantara. My name is Rebecca Knight, and I'm here with Dave Vellante, my co-host. We are joined by Nathan Hart, he is the Development Manager at NextGear Capital. Thanks so much for coming on theCUBE, Nathan. >> Thanks for having me. >> So let's start by telling our viewers a little bit about what NextGear Capital is, and what you do there. >> Sure, NextGear Capital is a, we do auto financing for auto dealerships, so if a dealer goes to an auction and wants to buy some inventory, we're going to be the ones who actually finance that and purchase it for them, and then they pay us back. >> Great, and your role as a development manager. >> Yep, I am over our integrations team, so we are responsible for basically getting data in and out of the company, a lot of that is getting data to and from our sister companies, all under Cox Automotive. >> And the data we're talking about is? >> Uh, it's a whole lot of things, obviously it's a lot of financial data, as we are a finance company, but a lot of things like inventory, unit statuses, where a car is located, we have credit scores, and that sort of work as well, so all kinds of data are coming in and out and then into our systems. >> So, are the cars instrumented to the point where you can kind of track where they are in an automated way, or is it? >> Yes, we do have some GPS units, not on all that inventory, just because we have quite a few open floor plans, about 500,000 I believe. But yes, we do have some select units that are GPS'd that we can track that way, or we have inspectors that go to lots. >> Okay so as a developer you know this story well, back in the day if you had a big data problem, you'd buy a Unix box and you'd stuff all the data in there and then you'd buy a bunch of Oracle licenses, and if you had any money left over, you could maybe do something, maybe buy a little storage, or conduct business. Okay that changed, quite dramatically. I wonder, if you could tell us your version of that story and how it's affected your business. >> Sure, so, uh. (laughter) >> Dave: Is it a fair representation? >> Not, not... >> Dave: Is the old world, was it a big data warehouse world? >> Yeah, so. >> Where it's sort of expensive to get stuff in and get stuff out and has that changed? Or is that sort of? >> Yeah, it has changed greatly, we're not quite that bad, but we do currently have an older monolithic database system that we are trying to get away from. >> Dave: It's hard. >> Yeah, exactly. And so a lot of our processes right now, go in and come out of this so obviously, if anything in that breaks, it hurts everywhere. >> Dave: Right. >> So yes. >> Dave: Sort of a chain reaction. >> Exactly. >> Okay, but so how have you, talk about the journey of bringing in Pentaho and how that has affected you. >> Sure, Pentaho has been great for us, just in terms of being able to be really flexible with our data. Like I said, we're trying to get away from this monolithic service, so we have, in Pentaho, we can easily branch off and say, go to the monolithic database, but also talk to another service that is going to replace it. And then it's just one click of a button, and now this is off, this is on, or we can do both and have some replication going, just so we have that flexibility, and that kind of adaptability around those changes. >> So why Pentaho, I mean, a lot of tools out there, there's open source, you could roll your own, you could do everything in the cloud, why Pentaho? >> We liked Pentaho because of the, I guess the freedom and independence it kind of offers, in the sense that it allows us to have a large set of steps and tools that are already prebuilt, that we can just use right out of the box, and, it's just a massive library, far greater than most of the competition that we looked at. And then it also is just built on this great Java platform that we can, if we need to, write a custom Java class, pop it in, and then that can do what we need to, if we don't have something out of the box. >> Dave: So it's integrated, >> Yep >> but it's customizable. >> Nathan: Exactly. >> If you need it to be. >> Nathan: Yep. >> Okay, and one of the things that customers like you tell us about Pentaho is that they like the sort of end-to-end integration. >> Nathan: Yep. >> We were talking off camera, you had mentioned that you've got an initiative to move toward the cloud. Maybe you could talk about that a little bit. >> Yeah, so right now, just Cox, as a whole, is kind of investigating the cloud. I definitely don't want to speak out of turn, or say we're definitely going there, but that is the current initiatives are to start experimenting with how we can leverage this more. I know one of the, kind of the first steps that we're taking towards that is we have large archives, we keep all of the files we've ever received or sent out, and we don't access them much, we don't need them much, but we want to keep them, just so we have this history, and we can always look back if we need to. So using the cloud for something like that, where's it's just like a deep storage, where we can just upload it and forget it, and if we ever need it, it's there and easily accessible, and this way we don't have to pay for as much storage on print. >> Very workload specific, cheap storage. >> Nathan: Yep. >> Probably a lot of test and dev. >> Nathan: Exactly. >> So going back to the Pentaho, and why Pentaho, and you mentioned the freedom and the flexibility that it provides, can you talk about some of the best practices that you've discovered that could help some other Hitachi Vantara customers? >> Absolutely, the biggest change, learning curve that we went through, my first introduction was Pentaho when I started at NextGear, and it was a real huge learning curve for the whole team. We all started within about a month of each other, and there were only three of us to start. So, it was a real learning curve of, okay, here's how we do this, here's how we do this. So, once we kind of got the workflow going and understanding what we were trying to do, the next step was figuring out okay we can make this very modular, we can build a sub job that does a very specific task, and we can use it everywhere. And we just did that again and again and again, so now we have a library of about 118 different utilities that we can just plug and drop anywhere and they just do what they need to do, we don't need to re-test them, we don't need to think about them ever. And of course, if we update one of those, it updates every single job that it touches. As soon as we kind of unlocked that and figured we didn't have to make a custom solution for every single job, that we could use a lot of reuseability. It really sped up our development, and how we do things. >> Could you talk about data sources, have they or how have they evolved over the last decade? >> Sure, I can't speak for the whole decade, I haven't actually been in the industry that long, but a lot of what we came into and inherited when I came in, were flat files, just everything is CSV, TXT, either in or out, and we still do a lot of that, that's still kind of our bread and butter, just by the nature of our current role, but as it's changing we are interacting more and more with APIs. We're shifting away from this monolithic database into micro services so we're having to interact with those a lot more and figure out how we can get that real time communication and get the data where it needs to go so it's all in its happy place. >> One of the things that Brian Householder, the CEO, got up on the main stage and talked about how, for companies, the two most important assets are the people and the data. I want to talk to you about the people aspect. >> Nathan: Okay. >> We're hearing so much about the shortage, the tech shortage of data scientists, and other kinds of talent in this industry. How hard is it for you to recruit? Your company, as you said, is based in Carmel, Indiana is that right? >> Nathan: Yep. >> What are you finding out there? >> The greater Indianapolis area, like many other places, is very starved for tech talent. It's very, very easy as a developer to throw a stone and get an interview. It's definitely a challenge. We actually currently have two openings on my team. Just, do less with more and do what we can. So, it's definitely a challenge, but I think that there's a lot of really great young talent coming out of colleges right now that are coming in, they've grown up with this right? They're a lot further along than necessarily I was when I came out of school and some of our other developers. So they can step in and already understand a lot of these complex architectures that we're dealing with and can just hit the ground running. >> So at least 10 times a week, I get somebody hitting me on LinkedIn about hey do you need development resources? (Nathan laughing) As a developer, it must happen to you 100 times a week, but there's obviously challenges of off-shoring and managing that remotely. I'm sure you've thought about it. What are your thoughts on off-shoring? You want someone there in a bee hive effect? Maybe talk about that a little bit. So, at NextGear we've been fairly rigid about butts in the seats, in the office, real collaborative environment, where you're at the morning stand up, you're there in the meetings, and it's a very present environment. And we are being a little bit more adaptable with that, just as time changes and other companies, obviously do offer more remote from home or what have you, so that is shifting a little bit, as far as necessarily off-shoring, that's way above my pay grade to even make that call, I have worked in previous environments where that was a large part of it. In a previously life we had a US based team and then we had a Malaysia based team, and I thought it was a really great experience cause we basically all had our own counterparts over there, so at the end of your day, you just email your notes, here's what I did today, here's where I left off, and they pick it up and do the same, then we had about a weekly meeting. So I think it definitely can work, I'm all for the global tech community all coming up together, when appropriate and when it works. >> But you've got to have the right infrastructure and processes in place, >> Nathan: Absolutely. >> Or it's just, it sucks all your productivity out. >> Nathan: Absolutely, if you spend half your day trying to figure out what the other person did, then you've lost your day. >> Yeah, right. And you follow the sun, yes and no right, you've got to wait for the sun sometimes. Pentaho, back to Pentaho, what are the things that, as a customer, you want them to do. What's on their to-do list, you know, when you're talking to Donna Prlich and her team, what are you pushing them for? >> So, the biggest things kind of on our wish list and that we're seeing is interacting more natively with those microservices like I mentioned and I was really glad that that came up in the keynote as something that they're focusing on and it's something that is going to come up in 8.0, at least the kind of stepping stones to go in that direction. So, that's really exciting stuff for us, just it answers a lot of questions we're currently having of how are we going to interact with those, and the answer can still be Pentaho moving forward. >> I was struck in the keynote, when Brian was asking hands up please, how many people are doing business with Hitachi outside of Pentaho, and just a smattering, right, I presume your hand was down. >> Nathan: My hand was down. >> And then, had you heard of Hitachi Vantara? >> I read the press release when they first announced Vantara, but that's about the extent of it. Obviously I knew about Hitachi from when they purchased Pentaho. We actually were having a week long, kind of a tech support get together that week that it happened, so I think on the Tuesday or something, our rep was like I now work for Hitachi. It was a fun thing, but yeah I'm not terribly familiar with Hitachi's products or, obviously I know where they're going with the Vantara concept, but. >> As a developer in a very focused area, >> Yep. >> Cox Automotive, obviously has some IOT initiatives, I'm sure, >> Absolutely. >> And some process automation, but I presume you haven't really dug into that yet, but when you think about the messaging that you heard this morning. What does it mean to you? Do you say, okay, nice, but I've got other problems? Or do you see the potential to leverage some of the technologies down the road? I definitely see the potential to start, at least exploring that direction, and figuring out what can we get out of this, right. It makes a lot more sense to play in a singular ecosystem and have all those tools at our hand just in one bucket instead of trying to figure out how does this play nice with this, how does this play nice over here, if we just can have a singular ecosystem that does it all together, that definitely makes our jobs a lot easier. >> How about the event, is this your first PentahoWorld? >> Yep, this is my first PentahoWorld. >> So it's early, but why do you come to events like this, and what do you hope to take away? >> Sure, I came to this event, cause I was specifically invited to. That's really it. It was nothing more than that, but I definitely come to kind of, see what's next and learn about the new technologies, and get that chance to visit some of the booths and some of the breakout sessions for maybe things that I don't get to do in my day to day life. We're very heads down in PDI so I don't get to spend too much time learning about the analytics and playing with those tools. So it's a lot of fun to come here and kind of see what's out there and be like, oh could we leverage this, or how could I adapt, or what are some of the other professionals doing that maybe I can bring back and improve our processes. >> And it's early days, but what are your thoughts on 8.0? >> I liked what I saw, and then I stopped by the booth and got another demo and I can definitely already see a couple of use cases where we can improve existing jobs with some of the new streaming features that they have in play, so I'm excited for that to come out and for us to start working with that. >> So that, the integration of streaming, Kafka, and the like was appealing to you? >> Yep, absolutely, and that'll be something that we can probably use right out of the gate, so excited for that. >> Well great, Nathan thank you so much for coming on theCUBE. >> Nathan: Yeah, thank you. >> I'm Rebecca Knight for Dave Vellante, we will have more from PentahoWorld just after this. (upbeat music)

(busy music) >> Announcer: Live from Midtown Manhattan, it's the Cube, covering Big Data New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello, and welcome to the special Cube presentation here in New York City for Big Data NYC, in conjunction with all the activity going on with Strata, Hadoop, Strata Data Conference right around the corner. This is the Cube's special annual event in New York City where we highlight all the trends, technology experts, thought leaders, entrepreneurs here inside the Cube. We have our three days of wall to wall coverage, evening event on Wednesday. I'm John Furrier, the co-host of the Cube, with Jim Kobielus, and Peter Burris will be here all week as well. Kicking off day one, Jim, the monster week of Big Data NYC, which now has turned into, essentially, the big data industry is a huge industry. But now, subsumed within a larger industry of AI, IoT, security. A lot of things have just sucked up the big data world that used to be the Hadoop world, and it just kept on disrupting, and creative disruption of the old guard data warehouse market, which now, looks pale in comparison to the disruption going on right now. >> The data warehouse market is very much vibrant and alive, as is the big data market continuing to innovate. But the innovations, John, have moved up the stack to artificial intelligence and deep learning, as you've indicated, driving more of the Edge applications in the new generation of mobile and smart appliances and things that are coming along like smart, self-driving vehicles and so forth. What we see is data professionals and developers are moving towards new frameworks, like TensorFlow and so forth, for development of the truly disruptive applications. But big data is the foundation. >> I mean, the developers are the key, obviously, open source is growing at an enormous rate. We just had the Linux Foundation, we now have the Open Source Summit, they have kind of rebranded that. They're going to see explosion from code from 64 million lines of code to billions of lines of code, exponential growth. But the bigger picture is that it's not just developers, it's the enterprises now who want hybrid cloud, they want cloud technology. I want to get your reaction to a couple of different threads. One is the notion of community based software, which is open source, extending into the enterprise. We're seeing things like blockchain is hot right now, security, two emerging areas that are overlapping in with big data. You obviously have classic data market, and then you've got AI. All these things kind of come in together, kind of just really putting at the center of all that, this core industry around community and software AI, particular. It's not just about machine learning anymore and data, it's a bigger picture. >> Yeah, in terms of a community, development with open source, much of what we see in the AI arena, for example, with the up and coming, they're all open source tools. There's TensorFlow, there's Cafe, there's Theano and so forth. What we're seeing is not just the frameworks for developing AI that are important, but the entire ecosystem of community based development of capabilities to automate the acquisition of training data, which is so critically important for tuning AI, for its designated purpose, be it doing predictions and abstractions. DevOps, what are coming into being are DevOps frameworks to span the entire life cycle of the creation and the training and deployment and iteration of AI. What we're going to see is, like at the last Spark Summit, there was a very interesting discussion from a Stanford researcher, new open source tools that they're developing out in, actually, in Berkeley, I understand, for, related to development of training data in a more automated fashion for these new challenges. The communities are evolving up the stack to address these requirements with fairly bleeding edge capabilities that will come in the next few years into the mainstream. >> I had a chat with a big time CTO last night, he worked at some of the big web scale company, I won't say the name, give it away. But basically, he asked me a question about IoT, how real is it, and obviously, it's hyped up big time, though. But the issue in all this new markets like IoT and AI is the role of security, because a lot of enterprises are looking at the IoT, certainly in the industrial side has the most relevant low hanging fruit, but at the end of the day, the data modeling, as you're pointing out, becomes a critical thing. Connecting IoT devices to, say, an IP network sounds trivial in concept, but at the end of the day, the surface area for security is oak expose, that's causing people to stop what they're doing, not deploying it as fast. You're seeing kind of like people retrenching and replatforming at the core data centers, and then leveraging a lot of cloud, which is why Azure is hot, Microsoft Ignite Event is pretty hot this week. Role of cloud, role of data in IoT. Is IoT kind of stalled in your mind? Or is it bloating? >> I wouldn't say it's stalled or that it's bloating, but IoT is definitely coming along as the new development focus. For the more disruptive applications that can derive more intelligence directly to the end points that can take varying degrees of automated action to achieve results, but also to very much drive decision support in real time to people on their mobiles or in whatever. What I'm getting at is that IoT is definitely a reality in the real world in terms of our lives. It's definitely a reality in terms of the index generation of data applications. But there's a lot of the back end in terms of readying algorithms and in training data for deployment of really high quality IoT applications, Edge applications, that hasn't come together yet in any coherent practice. >> It's emerging, it's emerging. >> It's emerging. >> It's a lot more work to do. OK, we're going to kick off day one, we've got some great guests, we see Rob Bearden in the house, Rob Thomas from IBM. >> Rob Bearden from Hortonworks. >> Rob Bearden from Hortonworks, and Rob Thomas from IBM. I want to bring up, Rob wrote a book just recently. He wrote Big Data Revolution, but he also wrote a new book called, Every Company is a Tech Company. But he mentions, he kind of teases out this concept of a renaissance, so I want to get your thoughts on this. If you look at Strata, Hadoop, Strata Data, the O'Reilly Conference, which has turned into like a marketing machine, right. A lot of hype there. But as the community model grows up, you're starting to see a renaissance of real creative developers, you're starting to see, not just open source, pure, full stack developers doing all the heavy lifting, but real creative competition, in a renaissance, that's really the key. You're seeing a lot more developer action, tons outside of the, what was classically called the data space. The role of data and how it relates to the developer phenomenon that's going on right now. >> Yeah, it's the maker culture. Rob, in fact, about a year or more ago, IBM, at one of their events, they held a very maker oriented event, I think they called it Datapalooza at one point. What it's looking at, what's going on is it's more than just classic software developers are coming to the fore. When you're looking at IoT or Edge applications, it's hardware developers, it's UX developers, it's developers and designers who are trying to change and drive data driven applications into changing the very fabric of how things are done in the real world. What Peter Burris, we had a wiki about him called Programming in the Real World. What that all involves is there's a new set of skill sets that are coming together to develop these applications. It's well beyond just simply software development, it's well beyond simply data scientists. Maker culture. >> Programming in the real world is a great concept, because you need real time, which comes back down to this. I'm looking for this week from the guests we talked to, what their view is of the data market right now. Because if you want to get real time, you've got to move from that batch world to the real time world. I'm not saying batch is over, you've still got to store data, and that's growing at an exponential rate as well. But real time data, how do you use data in real time, how do the modelings work, how do you scale that. How do you take a DevOps culture to the data world is what I'm looking for. What are you looking for this week? >> What I'm looking for this week, I'm looking for DevOps solutions or platforms or environments for teams of data scientists who are building and training and deploying and evaluating, iterating deep learning and machine learning and natural language processing applications in a continuous release pipeline, and productionizing them. At Wikibon, we are going deeper in that whole notion of DevOps for data science. I mean, IBM's called it inside ops, others call it data ops. What we're seeing across the board is that more and more of our customers are focusing on how do we bring it all together, so the maker culture. >> Operationalizing it. >> Operationalizing it, so that the maker cultures that they have inside their value chain can come together and there's a standard pattern workflow of putting this stuff out and productionizing it, AI productionized in the real world. >> Moving in from the proof of concept notion to actually just getting things done, putting it out in the network, and then bringing it to the masses with operational support. >> Right, like the good folks at IBM with Watson data platform, on some levels, is a DevOPs for data science platform, but it's a collaborative environment. That's what I'm looking to see, and there's a lot of other solution providers who are going down that road. >> I mean, to me, if people have the community traction, that is the new benchmark, in my opinion. You heard it here on the Cube. Community continues to scale, you can start seeing it moving out of open source, you're seeing things like blockchain, you're seeing a decentralized Internet now happening everywhere, not just distributed but decentralized. When you have decentralization, community and software really shine. It's the Cube here in New York City all week. Stay with us for wall to wall coverage through Thursday here in New York City for Big Data NYC, in conjunction with Strata Data, this is the Cube, we'll be back with more coverage after this short break. (busy music) (serious electronic music) (peaceful music) >> Hi, I'm John Furrier, the Co-founder of SiliconANGLE Media, and Co-host of the Cube. I've been in the tech business since I was 19, first programming on mini computers in a large enterprise, and then worked at IBM and Hewlett Packard, a total of nine years in the enterprise, various jobs from programming, training, consulting, and ultimately, as an executive sales person, and then started my first company in 1997, and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology, and I love covering emerging technology. I was trained as a software developer and love business. I love the impact of software and technology to business. To me, creating technology that starts a company and creates value and jobs is probably one of the most rewarding things I've ever been involved in. I bring that energy to the Cube, because the Cube is where all the ideas are, and where the experts are, where the people are. I think what's most exciting about the Cube is that we get to talk to people who are making things happen, entrepreneurs, CEO of companies, venture capitalists, people who are really, on a day in and day out basis, building great companies. In the technology business, there's just not a lot real time live TV coverage, and the Cube is a non-linear TV operation. We do everything that the TV guys on cable don't do. We do longer interviews, we ask tougher questions. We ask, sometimes, some light questions. We talk about the person and what they feel about. It's not prompted and scripted, it's a conversation, it's authentic. For shows that have the Cube coverage, it makes the show buzz, it creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the Cube, and that is the result of great content, great conversations. I'm so proud to be part of the Cube with a great team. Hi, I'm John Furrier, thanks for watching the Cube. >> Announcer: Coming up on the Cube, Tekan Sundar, CTO of Wine Disco. Live Cube coverage from Big Data NYC 2017 continues in a moment. >> Announcer: Coming up on the Cube, Donna Prlich, Chief Product Officer at Pentaho. Live Cube coverage from Big Data New York City 2017 continues in a moment. >> Announcer: Coming up on the Cube, Amit Walia, Executive Vice President and Chief Product Officer at Informatica. Live Cube coverage from Big Data New York City continues in a moment. >> Announcer: Coming up on the Cube, Prakash Nodili, Co-founder and CEO of Pexif. Live Cube coverage from Big Data New York City continues in a moment. (serious electronic music)