 Live from the Fairmont Hotel in San Jose, California, it's theCUBE at Big Data SV 2015. Welcome back everybody. You're watching theCUBE. We're live here at Big Data SV. It's Big Data Week in San Jose, California. I'm joined by John Crysis, the VP of Strategic Marketing at Hortonworks, and Chris Tugut, who's the VP of Product and Services Marketing at Teradata. Gentlemen, welcome to theCUBE. Thanks, Jeff. Both frequent guests, well, having you back. Thanks for coming. So we were just talking a little bit off air. Obviously we've got Hadoop roles going on. What are your impressions? You guys have been at the show. Is it a good vibe there? What's going on? We start with you. Yeah, I think there's, you know, it is a good vibe. I think there's a lot of great activity. I mean, you know, customers out there looking for solutions. I think customers really trying to solve some of the core issues that they have around metadata and security and around how do I kind of, you know, get my infrastructure ready to go and ready to scale and really, you know, get big data pervasive in the marketplace. So I think it's got a good buzz to it. Yeah, from talking to some of the other guests who've come through, they said it's starting to see more focus on move to the applications a little bit more than some of the infrastructure. So that's kind of tells you that the market's moving in the right direction. But why don't we talk a little bit about the relationship of Hortonworks and Teradata? So you guys have been partners now for quite a while. I think Hortonworks was your first Hadoop partner, really, Chris. Talk a little bit about, maybe just take a step back, tell us a little bit about how the relationship developed and maybe we can talk about where we are today. Yeah, that'd be great. I mean, we started this relationship about three years ago. Yeah, that's right. And, you know, it's really based upon a foundation around how can we really extend the value of the ecosystem to our customers, right? With all this idea about big data and volumes of data and how can we work together in a joint development model to really bring value? And so we've done a lot of this. I mean, we've integrated with Viewpoint, which is our single plane of glass to be able to monitor and administer the Hortonworks HTTP. We've deployed a joint appliance and happy to have customers in a broad range of industries with that. I mean, in manufacturing and retail and finance and really as a way for people to have the fastest time to production. Really being able to bring it in, deploy it, ready to run and it's got pre-configured HTTP set up and they're really able to get up and running very, very quickly. And then we've continued to innovate. You know, if we look at innovation that Hortonworks brought together with H-Catalog in our first introduction with SQL-H and then extending that to query grid and pushing down. So it's been a great relationship, not just from a technology perspective, but I think also just from a go-to-market and a philosophy and our management teams work really well together. So I think it's been great. No, I would agree. I'd echo that. I think the engineering, joint philosophy around engineering the technologies together in order to provide additional value for the customers. As Chris said, really making sure that they can utilize both infrastructures in a way that really benefits them the most. And I think their data really has leaned in from the very beginning in terms of having an engineering relationship with us and making sure that that was always a very tight connection between two technologies. You mentioned on previous segments how these partnerships are very much engineering driven. They work their way up the ladder after that but they start really very much at an engineering level. Maybe could both of you talk about what does that look like on the ground? When we talk about tight engineering partnerships, what does that practically look like? Sure, and I can start, I think that it starts with commitment by both engineering teams and product management teams. It's not just the engineers, but also those that are making the plans and roadmaps for where the products are going. Need to meet regularly, have joint planning meetings, synthesize the roadmaps in a way that would drive the most benefit for the customer. So Chris mentioned a couple of query grid integration with H-Catalog and those kinds of things. Early on, Mbari integration into viewpoint, it's really those teams working together to synchronize releases and synchronize development schedules and roadmaps in order to bring value to the customer. You know, I would echo that. I think it is those quarterly engineering meetings where we get engineering leadership together. And it is sinking roadmaps, but it's also saying, right, where are you going fundamentally, Hortonworks, and Teradata, where are you going fundamentally? And how can we leverage the skills of each organization to bring value to the marketplace? And really an extension of that. I mean, all of the acquisitions that we've done, you know, with Revelytics and Loom, with Rainstore and the archiving solution, both of those running on top of HDP. You know, with the Think Big acquisition that we did, really services around what can be done within Hadoop and specifically HDP and how we deploy data lakes in the marketplace. So we're continuing to build on those early days and then bringing it to market. I think it's those core engineering meetings that really help us sit down and map out where we each can bring significant value out in the relationship. And how does that, such tight integration help? Teradata, as we've talked before about one of the value adds that you're bringing to your customers is helping enterprises kind of bring this all together so that you can integrate kind of the data warehouse with Hadoop, with some of the other new approaches into a comprehensive platform that you can actually leverage, get the most from your data and wherever it happens to live. How does the engineering partnerships help you kind of deliver on that? Well, because not only do we build out engineering, but we also make sure that these things are very performant. I mean, just recently we did a, we both won a joint customer. It happens to be an internet customer in the publishing space. And they actually said, look, I want to see how Teradata and Hortonworks works well together and we deployed query grid within that environment and we literally were able to ingest a query through Teradata. We pushed it down to a billion row table sitting inside of Hortonworks, processed it, delivered a 10 million record back, joined it with the rest of the data and that whole thing happened in six seconds. I mean, the reality is it's the high speed, parallel, tight integration and the dynamic and flexible deployment of how we bring these solutions together. So it's almost like they're not two different solutions that kind of meld together to solve the customer's problems. Oh, to add, sorry, it's just that the reuse of skills. So the fact that you can have the skills in the data warehouse and be able to run those jobs, as Chris said, and push it down into the Hadoop infrastructure without having to know the details of where that sits also helps accelerate the adoption of the technology through the unified data architecture and through the kind of jointly agreed to vision of how it'll be integrated and how the customers can best consume it. We've often said, business users don't care is the data sitting in Teradata is it Horton and Hadoop, they don't care. They just want to be able to run a query and get an answer back. I think together we're able to help solve those problems. That's a really good point. And in this world that we're living in today, it requires, I think, for kind of new business models on the vendor side. Where Horton works, the partnership and open DNA is it was kind of born in your DNA upon birth, whereas Teradata and some of the other companies are coming to have a much longer legacy. Is it a challenge to kind of adapt, I should say to these new kind of, more open business model where you've got to cooperate with different vendors to really help each other drive value and essentially open up the market. From a Teradata perspective, not really. Because it's interesting, Teradata, we've always been a player in the marketplace that says we want to optimize with best of breed technologies out there. We've never had our own BI tool or our own data integration tool. We've always had best of class integration with Informatica's and the Talans and best in class integration with the tableaus and micro strategies and business objects. Frankly, a lot of those companies have been absorbed by others that you would consider our competitors. So this has really been core to our strategy is just do best in class integration and then allow customers to choose. What is the best solution for you in the marketplace and we extend that into the Hadoop environment. So it's worked well for us and I think it continues to work well. Yeah, I mean, case in point is the open data platform, which is the big news of the week, essentially trying to help standardize and push adoption of Hadoop into the enterprise. Hortonworks, of course, one of the founding members as well as Teradata. Talk a little bit about that organization and kind of together, where do you see that going? Yep, so I'll say just one thing to make sure it's clear the open data platform initiative is really about driving the consumption side. It's not about this intermediating any of the other components. Of course, still continue to do all the upstream development in ASF and work through those appropriate mechanisms. The ODP is really about helping drive the consumption and providing a common core for consumption by enterprises so that really the ecosystem has something that they can work together and work around a common core. And so it's something that we'll work very closely with Teradata as we have. We have a great long-term engineering relationship and I think this is something that just sort of shows how we have already been working together now and we'll continue to work together going forward. I think the open data platform initiative is really strategic and we talked a bit about it before, right? I think certainly Hadoop is open, but it is fragmented, right? This is you have different distributions popping up and starting to add different value add. And as we look at this space, our strategy is to really make it simple around helping to manage the broader analytical ecosystem. And if you have standards and APIs and integration, not only does that make it easier for Teradata, but it makes it easier for our customers to integrate and adopt. And I agree with what John said is it's really all about the consumption layer, right? And it is all about driving adoption of open source. I think there's some people that have read about this and are like, wow, this is gonna be a closed source thing and vendors and they're gonna take control. For me, this is not about control of anything. It's about furthering the consumption, the usage of the technology into a broader environment. And so I'm excited about the initiative and I think it'll be good for the industry. But I think it'll be good for customers. Well, speaking of customers, so let's talk about what you're seeing among your joint customers. What are some of the more common or more valuable use cases you're seeing among Hortonworks and Teradata joint customers? I'll say one of the biggest drivers from a joint customer perspective is this notion of a data lake. And I think it is a bit of an evolution. We talk about how early on deployments around Hadoop were typically in lines of business, right? But this idea about a data lake really is an architectural pattern for bringing in data in its original fidelity from a broad set of sources and then being able to understand and consume it and then have different users accessing it. I think is really something that's needed in the marketplace. I mean, even with Teradata as a data warehouse, we never took data in its original fidelity. We would take data, we would transform it and then we would put it in. We always put relevant data into Teradata and we'll continue to do that. The data lake is about consuming all of the data. Some of it might not be relevant today, some of it might be relevant tomorrow, but having it there that can be used and refined and consumed is an important dimension. So we see a lot of customers really on the verge of deploying data lakes in a really strong way. But there's still a bit of evolution in terms of things that need to be done around governance and metadata and security and archiving. Cause all of the core data management rules still apply to data lakes as they do to data warehouses. No, I would agree. I think that it is an evolution, a journey to get all the way to a data lake. It'll start with, as Chris said, one or two use cases potentially in different business units and they'll ultimately get to that pattern. I mean, a good example of where somebody can get to, there's a large retailer that we have as a joint customer who their goal is to get to a kind of a golden record, a 360 degree view of the customer that encompasses a lot of the other data types and at the full fidelity, yet leveraging all the different components in order to get that view of the customer, whether it's the operational side or the other way that they might interact to the customer's social or loyalty cards and those kinds of things. So this large retailer, that was their goal, was to use Hadoop to land the data at the absolute level of fidelity, but integrate it and utilize the other technologies they had, including teradata technologies, in order to get that kind of common view, single view of the customer and exploit that. Well, I think it's an important point to make that this kind of the new world of Hadoop is not necessarily competing directly with the data warehouse world. I mean, there's going to be some overlap and there's going to be some competition there, which makes this market, you know, interesting and exciting from an analyst perspective, but it's also about helping companies and your customers get more value out of the investment they've made in teradata. Absolutely. Because now you can move some of that data that you couldn't, you wasn't practical to either store or process and bring that into a teradata environment where you can actually do some more of the types of analysis and put it into mission critical reports that you're already running. So I think that's important, you know, to point out. So going forward, what's, you know, to the extent you can share, what's on the joint roadmap between teradata and Hortonworks, what are some of the things you're working on and you're, you know, if we're here at this table next year, what are some of the things you think we'll be talking about? Yeah. Well, I think together we'll be really solving some of the core challenges around the Data Lake. You know, like I said, Data Lake is a really good architectural pattern, but there is some maturity that we need to drive inside of the Hadoop infrastructure to be able to really, you know, capitalize on the value of having a Data Lake that can really scale. And I think that that has dimensions of, you know, need for doing metadata and data lineage and data wrangling and self-service. And, you know, we announced, as we talked about, teradata loom. And that plays a key role running on top of HDP. We think, you know, archive. And some of these things that, frankly, people have not been thinking about within Hadoop. But if you have an infrastructure that you're using in a consistent way and you need to make sure that you archive and you back it up and you've got something that has high compression. So some of the stuff that we've done with our acquisition of Rainstore. And then continuing to drive activity with Think Big to optimize Data Lakes. Because it's not just about the software. It's also about services to make sure that they're applying the right kind of principles. And Think Big's been doing this for four years with over a hundred different implementations. So we'll be working closely together and in those areas, just continue to build it out around the value add, but also in terms of a go-to-market model. We think a Data Lake is a great architectural pattern and we think all of our customers should have one and we're going to work to go help drive that. Yeah. No, I would totally agree and echo that. And, you know, the companies that Teradata has acquired were partners of Hortonworks already. On ones that we were already starting to work with. You know, under the Teradata umbrella, it gives us a chance to work even deeper engineering relationship. Loom is a good example with the data governance initiative that we have. You know, getting Teradata to help integrate that technology even deeper into the platform to solve some of those issues that the enterprise are looking to do as they expand their deployments into this Data Lake-like pattern. So they are real issues. They're ones that the customers are asking us to make sure we solve and I think that's one that we can solve together. Well, you mentioned acquisitions and which, you know, would like to kind of get your take on what is the acquisition strategy at Teradata? When do you look to make acquisitions around? Chris? Around. I'll be telling you the next 30, they weren't going to. Yeah, tell me that. Acquisition strategy around... They strive, don't you? Got to try, right, folks? But around acquiring versus partnering. Do you look at, is there carriers where you need to, this is something Teradata needs to be bring into the Teradata fold, whereas these are areas where we need to partner? That's what I meant about how do you look at that? If you want to share the next few, that is a difference. No, no, I think it's a really good question and I actually think if you look at the ones we've acquired, it fits very well into our strategy. I mean, a lot of people don't know this. I mean, Teradata does well over a billion dollars in services in this marketplace, so it's not surprising for us to go, extend in terms of Think Big and have services around open source and emerging technologies. When you look at some of the core competencies that we've had in terms of bringing optimizers and SQL engines to the marketplace to run at scale and MPP, I mean, typically what we do is we look at, okay, where's a core competency that Teradata has and then how can we extend that core competency with acquisitions that fit well into what we do? We don't spend a lot of time looking at and acquiring companies that have BI tools and visualization in front end or DI tools. That was never part of our strategy, but technologies that surround the core infrastructure and make it easier to manage and simpler and technologies that bring the analytical ecosystem better together, you'll absolutely see us continue to invest in those areas. Okay, well, I had to try, right? Yeah, I tried. All right, guys, well, thanks so much for coming on. I appreciate it. Congratulations on your success together. You guys are doing some good work. Congratulations on the ODP and we'll be watching. This is a really exciting market and we'll be watching closely and we'll see what those next acquisitions say. Sounds good. Thanks for joining us. Thanks for watching. Stay tuned, we'll be right back with our next segment here live at Big Data SV in San Jose right after this.