 Okay, so what about Hadoop World? Why are you here? What are you doing at the show? Well, I think Hadoop is obviously a great trend, a very big trend in the market. We're getting a lot of interest from people that are either using Hadoop or they're thinking about using Hadoop. And most of the time, the interest we're getting is that people love what Hadoop is promising. They love MapReduce. They want to be able to do deeper analytics. On the other hand, they're trying to figure out what is really good use case for Hadoop. What is not a good use case? And how can they reduce the overall total cost of ownership? Because the traditional way of deploying Hadoop, which is, you know, you have a whole bunch of engineers and you deploy them to manage Hadoop, do the analytics, everything together, that's a model that works for a few companies like Facebook, but has a very high overall cost. So what Enterprise are thinking today is how can I deploy MapReduce? How can I do this big data, new type of data processing, but with a more controllable, more reasonable cost? And that's where we can complement Hadoop as a solution. So I talked to a number of TerraData customers, not just TerraData, but other Enterprise data warehousing customers. And they describe what they go through as a sort of a snake swallowing a basketball, right? And they're constantly ingesting more data and they can't keep up with it. And you know, that's one of the problems that Aster, I guess, set out to solve. Correct. And so I want you to talk about that a little bit. And then talk about the unique requirements of Hadoop. So start there. Tell people a little bit more about Aster data and how it's different as a platform. And then we want to talk more about Hadoop. Yes, absolutely. So what Aster is doing is Aster offers implementation of MapReduce, which is a language you also find in Hadoop and is the component Hadoop that allows you to do deeper analytics, but we combine it with SQL. So we have a patented framework that's called SQL MapReduce that allows you to do MapReduce analytics, but access those through a standard ANSI SQL interface. What this means is that business analyst enterprises can do MapReduce analytics just like they could do SQL queries and SQL reports. And you can also connect a lot of third-party ecosystem tools like BI tools, ETL tools to MapReduce through the Aster platform. So the way to think about Aster is that Hadoop is a great platform to ingest data, archive data, and do batch transformations. But Aster is a great platform to do a lot of iterative discovery analytics. If you have data scientists that need to quickly do a number of analytics, iterate through the data, and expose those analytics to business analysts or BI tools, Aster is the only solution in the market that can do that. And in fact, we've gotten some tremendous traction, tremendous press around SQL MapReduce technology. And even actually, certainly this weekend there was an article on Forbes.com that was talking about technology and everything that you can check out. But really the combination of SQL and MapReduce is what it takes to bring the part of MapReduce to the enterprise.