 Okay, we're back here live at Oracle OpenWorld. In San Francisco, California, this is theCUBE, our flagship program about the events that trickle through the noise. I'm John Furrier, the founder of Silicon Am. Join analyst from wikibond.org, Jeff Kelly. We're here with Manan Goyal, Senior Director of Product Marketing at TerraData, Aster TerraData. Welcome to theCUBE. Thank you. So, obviously big data is going to be a big part of the RedStack and the OpenStack and the open source community. I want to get your take on what's going on in Oracle. First, your impressions of the show. Obviously, the keynote is always the best. We'd love to watch the live stream and also the Twitter stream. What's your take so far around what Oracle's doing with respect to the middleware and then above the engineered systems around the big data opportunity? Right, so this show is, as you know, the biggest show in the tech industry. So, it's always exciting to be here. We are here as TerraData. We're participating. We also have Aster Presence here. We bring a very different perspective from Oracle's around big data and discovery and analytics. So, we'll be talking about that throughout the show and on this. Jeff, I want to get your perspective on the big data angle, because obviously the angle on big data has always been, hey, we enable you purpose-built systems, engineered systems from Oracle. What's your take as an analyst and here with the big data story? Is it kind of like ho-hum? Is it off the charts? What's your, what's your, what's the sentiment perspective? Yeah, well, I think Oracle is clearly saying, look, big data drives a lot of business value if you can actually leverage it and use it. And part of the challenge we're seeing out there with practitioners is, it's very difficult to get, for instance, a Hadoop cluster up and running optimally to start building applications on top of it. To do advanced analytics on how the data scientists might find some insights, you want to then productionize that. So, that's not an easy thing to do and to integrate and to actually get everything running optimally. So, Oracle's really value proposition, their message is, look, we're going to take all that complexity out of the equation for you. We've got engineered systems, we've got the hardware, the software, drop it into your data center and start doing some of the analytics sooner that's really going to drive business value. Now, that comes with a price, however. So, one of the real benefits, of course, of the big data open source approach is that you've got commodity machines, you've got open source, sometimes free software, and you can do it at a much less expensive price point than you can for a traditional Oracle box. That obviously is taken out of the equation if you go with Oracle's exit data or the big data appliance, et cetera. So, you know, but what Oracle is saying is, look, yes, you could do it cheaper, but we can make sure it's up and running. We take away some of that risk. We integrate it with our other database systems, our applications that you're already running, the rest of your organization with. So, it's a fairly compelling value proposition, a fairly compelling argument. You know, flies in the face of what we're seeing in the open source world. But, nevertheless, you know, I think Oracle's going to have some success with their install base. Oracle's, you know, ubiquitous in the enterprise. So, you know, it'll be interesting to see what happens. You know, of course, another player that gets talked about a lot is, of course, Teradata. And you're from Teradata from Aster, an acquisition a couple years back around bringing in the Aster SQL MapReduce platform to kind of complement some of the data warehousing, a strength that obviously the Teradata has. So, tell us a little bit about, tell us a little bit about where MapReduce, excuse me, where Aster fits into the Teradata story if you're all around big data. You know, there was a couple of announcements earlier this year, the, Tim, the Intelligent Memory platform, there was an announcement around your Hadoop portfolio. Where exactly does Aster fit in? Okay, so Jeff, Aster is a discovery platform. So, what we mean by that is, you know, Aster empowers our customers to quickly bring in diverse data sets, you know, that data from transactional sources, newer forms of data from web logs, clickstream data, machine logs, sensor data, and analyze that using diverse analytic methods and techniques. So, what Aster does really well is, you know, enables our customers to look at that data through multiple lenses, if you will. So, a lens of SQL, a lens of MapReduce, a lens of graph or machine learning, and statistics, all in an integrated fashion, and delivers much richer and powerful insights from that. So, you mentioned, you know, the challenges around getting value from big data. You know, it's definitely, big data has a lot of value in it, but getting value is extremely difficult with, you know, the technologies that are out in the market today. So, that's where, you know, at Aster, our perspective is we are trying to make discovery and finding these insights in big data really easy for the customers. So, one way we are doing that is, you know, at Aster, we have standardized around unified SQL interface, as a interface for customers to work with all these different analytic engines, if you will. So, in our case, in our Aster platform, for SQL, for MapReduce, for graph, SQL is a unified interface through which, you know, all of those capabilities are exposed. Plus, we also provide, you know, pre-built functions, a rich library of AD plus pre-built functions, which really make it easy for our customers to, you know, explore big data and get value out of it. So, at Aster, we are all about making it easy for customers to do big data discovery and exploration, and we are also about combining all these different analytic techniques to deliver much richer and powerful insights. So, just to give you an example, you know, a customer of ours, they had, they were using statistical techniques to predict customer churn, and they were getting some, you know, some churn lift from their models. But we brought, the customer brought in Aster, they started looking at their data in a different way, you know, looking at customer behavior, customer experiences, customer journey, and they were able to improve the yield of their churn models by 25%. So, just by combining, you know, two different analytic techniques, like SQL path pattern and statistics, they were able to get much more richer and powerful insights, and that's what Aster is all about. So, really, you're taking an approach where it sounds like trying to enable the analysts to do the type of analysis he or she wants to do, and whatever approach is best for that particular use case. So, you mentioned kind of, you know, the SQL interface, the SQL kind of as being the door into some of the type of analytics that analysts want to do, and we hear a lot about bringing SQL to Hadoop, for example, from Fader and their Impala engine, Hortonworks with their Hive, the Stinger Initiative and others. So, is that, is really, is that what Aster, is Aster's play in that space? Is that essentially your approach to making big data accessible to analysts and other business users? My understanding of that, right? Yes, so we do have a solution, you know, a solution that we call SQL Edge, around, you know, connecting to Hadoop. You know, we support the common Hadoop distributions and you can run SQL queries on top of Hadoop. But our solution, our discovery solution and our platform is much more advanced, you know, just from providing basic SQL capabilities. Couple of things that we are doing is, we are taking these capabilities like MapReduce, like graph processing, like machine learning, that are these newer technologies and really making them the first class citizen on our platform and providing this true integration. So, from an analyst perspective, what it means is that, you know, an analyst, you know, using a plain, easy, open standards language like SQL would be able to write one single SQL query, which may include a MapReduce function in it, which may include a graph function in it, which may include a simple SQL statistical function in it and, you know, just by blending these ensemble of analytic techniques together, they can, you know, much easily, much more effectively analyze the data and get insights out of that. So, our approach is much more of a strongly integrated approach as opposed to, you know, as opposed to what some of our competitors are doing where they're making things work in more of a coexistence model. They are more focusing around the coexisting model, but still you need, you know, like, different skill sets, different solutions, whereas we are going with more of an integrated approach with unified SQL interface and a single query language and out of the box functionality. Jeff, I want to get your perspective. One, I see Hortonworks and CloudEra both are the players in the Hadoop, you know, Hadoop equals Hortonworks, Hadoop equals CloudEra. In the mind of the world, that is essentially the open source philosophy. You guys, these guys have a relationship with Hortonworks. They got intel out, they got their own distribution. There's all kinds of discussion around that. I want to get your take on, does that world want to collision with the Oracle world or the Oracle world, looking at this like, hey, we got CloudEra, I mean, what, how is Oracle dealing with the massive growth of open source and big data? I'll see, scale out, open source, where's the Oracle, scale out, purpose built. Well, you know, they're trying to co-opt the trend as they've done kind of with cloud, right? So, you heard in the keynote this morning, Clutter actually got a mention of being, you know, the Hadoop distribution integrated into, sorry, Oracle's big data plans. So I think what, I mean, from my perspective, and it's a little cynical, I'll admit, but you know, I think Oracle probably has the most to lose potentially from the open source big data approach. It's really antithetical to what Oracle does, which Oracle's about proprietary, scale up, expensive integrated systems, where the open source big data world is about, you know, commodity machines, pre-software, scale out. It's really quite different, and it really would potentially disrupt Oracle's business model. Now, Oracle's not going anywhere anytime soon, I don't mean to suggest that. But I think Oracle understands that they've got to start adapting a little bit more to this world, which is why they brought in Clutter. You know, I think last year, last year's open world, it was more just kind of messaging a little bit of lip service around Hadoop and NoSQL. They're talking a little bit about it more, they've added some more functionality and some more connections between their different systems, so it seems to me they're taking it seriously, but ultimately, if, you know, as an enterprise, I can get a lot of the functionality and a lot of the new big data analytic capabilities from whether it's AstroData or others at a much lower price point, then Oracle's offering me a very expensive Exadata box. You know, that's something I got to take seriously. Now, certainly Oracle has some value of their own, like I mentioned at the top of the segment. You know, they de-risk, to a certain extent, big data deployments. You don't have to worry about doing that integration work yourself, but it's expensive. Well, we'll have Mike Olson on from Cloud Air tomorrow at four o'clock. Also, Max Shearson, the president of MongoDB, both guys in the database world, obviously in the whole open source community, is going great. Lennon, I want to ask you a question back to AstroData and Teradata. What is your approach? What are you guys doing with the Hortonworks? Give us the update on the relationship with Hortonworks, and how does that all fit in with your business? Absolutely. So, first of all, you know, like I would like to second what Jeff was saying, you know, like you look at Oracle, you look at Teradata, you know, the Hadoop guys would want you to believe that, you know, that is the next data management platform. And then, you know, some of these technologies, like, you know, Oracle or Teradata are going to go away. But that's not what we are seeing in our customer base. What we are seeing in our customer base is, you know, customers will continue to use, you know, Teradata as a data warehouse platform. But then they are adding, you know, these additional technologies, like Astro, like Hadoop, for workload specific applications within their platform, within their analytic infrastructure. So, what we are seeing is, you know, we are going to market with Teradata as a solution. You know, we're pitching the unified data architecture to our customers. Unified data architecture includes Teradata's data warehouse, it includes the Astro Discovery platform, and it also includes Hadoop distribution. And we see all of these technologies having, you know, specific roles within the unified data architecture. So, these, you know, Hadoop from Hadoop is a great data landing area, a data lake, sort of a thing. Astro is primarily around discovery, finding insights or nuggets in this big data. And then finally, when you operationalize or productize these insights, that's where Teradata comes in. So, we are seeing, you know, these three technologies working really closely together and, you know, providing customers with workload specific processing applications at the right economics and for the right data and platform. Chef, what's your take on that? Well, you know, I think I agree that, you know, the idea that Hadoop is a cure-all is pretty much, you know, it's not the case. I mean, I think Hadoop has its place, and it's a very important foundational technology. I think in a big data platform, that it really allows and really tests the technology that kind of ticked off the whole big data movement. Really, because it allows you and really just took the storage capabilities and just expanded them significantly because of the low cost of storage. And you can do a lot more processing on that data, kind of eliminates the idea of sampling. You can look at all your data depending on the use case. But I agree, there's different use cases, there's different tools for each use case, and you've got to use the right tool for the right use case. For the enterprise, the key is identifying which tool is right for which use case, and then integrating those into a cohesive platform that allows you to not just get insights, but then operationalize those. And then to close the loop, a feedback loop, where you've got some analytics, you've got some applications that are leveraging those analytics, and then feed that back into the system to see what's working, what's not working, and continue to iterate. So that integration work, I think, is really important inside the enterprise. And, you know, we're still very early in this big data world, still a lot of just experimentation going on, especially when we think about the Hadoop world. So we're not seeing a lot of enterprises get to that point where you've really got a closed loop system. But it'll be interesting to see how that happens over the next five to 10 years, and how companies like Teradata, how companies like Oracle actually help their customers make that happen. And it's the vendors that actually have a vision around that, I think, that's going to be important over the next decade. Menon, I would like to talk a little bit more about really how the Aster business specifically is going. You know, you talk a little bit about how you're integrated into the larger Teradata platform, unified platform. So tell us a little bit about the strategy in terms of Aster as a standalone platform. Are you seeing more traction as Aster being brought into Teradata deals, or is there still a lot of demand for Aster as kind of a standalone platform in heterogeneous environments? Maybe there's no Teradata in those environments. Maybe there's Oracle, or IBM, or whoever. So, Jeff, I would say we are seeing both of those trends, you know, like there's certainly a role for Aster within the unified data architecture in conjunction with the Teradata data warehouse to do more analytics and discovery around big data, these newer data sources. But then there is also as a standalone solution, you know, we are seeing really interesting use cases being developed around Aster, primarily around discovery, you know. What Aster is enabling customers to do is to quickly bring in data from these diverse data sets. You know, like your transactional data, like web clickstream data, and analyze that data through multiple analytic engines, like SQL, MapReduce, Graph, all in integrated fashion. And from a use case perspective, customers are able to find really interesting patterns around customer behavior. They are able to better manage customer experience. They are using Aster for customer churn. That's a big use case for us. Fraud detection, you know, identifying and cutting down ways and those kind of things. So we are seeing, you know, globally, we are seeing really interesting use cases of big data analytics and discovery emerging across different industry segments, different verticals, and seeing a lot of fraction around Aster and with UD and Teradata for that space. So just one last question for me. I'd love you to tell us what you've got kind of coming up going forward. To the extent that you can, I know sometimes these are closely held secrets, but what's next, what can we expect to see from Teradata generally, but Aster in particular? Yeah, so Jeff, we are getting ready to make a new product announcement here in the next couple of weeks. So we have a new, we have a launch of Aster Discovery Platform coming up. This to us is really fascinating and major release. This is the next generation discovery platform, if you will. So you know, when we talk to customers, they tell us that these diverse data sources, these diverse analytic engines are really important to them. So that's where this new release of Aster Discovery Platform will bring in new analytic engines, new data stores, and also make these engines work really well in an integrated fashion and through a common user interface like SQL together. So that will enable our customers to gain even much more richer, powerful insights and make better discoveries and use those insights to innovate and optimize their business processes. All right, well, fantastic. We look forward to that. Okay, this is theCUBE. We'll be right back with our next guest after this short break. I'm John Furrier. This is our flagship program. We'll be right back. Live in San Francisco for Oracle Open World 2013. We'll be right back.