 So we are here with Splunk. We have Sanjay Mehta from Splunk. Here's to cover a little bit. And they just announced Hadoop integration last week. And so we're really here to learn more about how you guys are integrating technology with Hadoop. And also wanted to hear a little bit about your englobic data because it's something that you've been involved in for a while. Yes, this one was formed in 2004. And the problem we really set out to solve was to collect and harness all of the machine data that is generated by systems that power businesses. And what I mean by that are the application servers, network devices, security devices, all those things, all those components that sit within an IT infrastructure, or even outside of an IT infrastructure, generate data all the time. Now the question really was what to do with that data because it holds a, it has intrinsic value. It holds the record of all activity and behavior, whether it's customer, your customer's behavior, whether it's user transactions, whether it's the actual behavior of the machines, the applications, the networks, things like that. And so we've been dealing with this challenge around how do you effectively collect and harness all of this high volume unstructured or badly structured, data which is generated in, you know, when we talk to our customers, one thing we hear all the time is that machine data is the fastest growing, most complicated, yet most valuable, segment big data. And so what we really set out to do with our most product shift in 2006 was to really provide the ability to harness the data. And the way we do that is we collect it from any source. We index the data and what indexing does is it actually makes that data for us all usable. You can run, you can interact with the data and we provide a search now, you can visualization layer where you can actually run searches against the data. You can look at patterns or for patterns of activity in real time. And you can also visualize it in real time as well. So you can create dashboards, you can generate reports, you can send those reports PDFs to your managers or bosses or whoever's interested in the data. So that's the thing we've been solving. And then what we've been hearing as well, we've got 3,000 plus customers, by six customers. We have many more customers who download the free product. We have a free download model. So you can actually use the product for free. And what we've found is customers that use our technology will do are asking for specific things. They're saying, can we use Splunk to collect all the data and then run analytics on it in real time as it's coming in. And even historical data as well. But then at some point, I want to transfer that data into HDFS for archival or for specialized algorithms that you can run as some doob jobs. The other thing they're asking for is saying, well, as I express a search, I want to be able to call the doob jobs and get the data from that back into my Splunk search and then present the results together. And then once it's in Splunk, you can visualize it, you can basically look at the data in different ways. And the other thing is I'm going to take the output of the doob jobs. So the doob jobs themselves are generating output files and do something with that data. So you can index it by Splunk and have Splunk to index that data. Once it's in Splunk, you can again, very quickly search and visualize it. Yeah, and when you talk about machine data and its growing importance and really look at some of the areas in which the industries that are looking to really put this technology to work. You're seeing a lot of real world use cases in the social sphere. Even here at the sessions, there's a couple of social sites, Facebook, a couple of others that are actually putting this to work. Some of their features are already live on the site. So when it comes to really pulling that value, they seem like they're ready, willing and able to kind of jump on that. And when it comes to your business model, bringing something like you into something aspect of your growing business. Yeah, I think for our perspective, where do customers see the value? And I think that's a really good question to ask is once you've got this data, how do you use it? And there's lots of examples, or there's a few examples of the, which really are the examples that led to the technology being built in the first place from the Google to the iOS world. I think our, what we're focused on is how do you democratize the data and get it out into mainstream use cases. And the mainstream use cases around historically are more around the IT side. So it's helping manage applications. It's helping support security or provide a better security posture, meet compliance mandates, manage and monitor your infrastructure. And then increasingly we've started to see uses because the same data as I mentioned earlier on holds all of that categorical information around who's doing what. What are the machines doing? But what are the people that are interacting with those machines doing too? So the other thing as well that we're seeing is that people are starting to use increased intelligence analytics from their operational data. They're saying, how's my website being used? How are streaming assets that are going through my website? How are they being used? I need to know that to be able to reconcile my royalty payments. How are my core records going in the telecom perspective? Because to us it's all just data. And whether it's a Twitter stream that's human generated in terms of the tweets themselves, or whether it's log files from machines or applications, all of that to us is just data. And we don't impose any type of schema ahead of time. So we can take that data in real time and then allow you to just look at it and ask questions that you may not thought of yet. We may have thought of just now and get an answer to that question. And that's really what we're focused on. And so with Hadoop, what that gives us is the ability to use the benefits of the plan, but also then have it do available for specialized algorithms and processing. But also if there is data in Hadoop, that isn't in this plan. And we want to be able to bring that into space that you can actually use that stage as well. So we see it very complimentary. I agree. Well, it's certainly great to hear what you guys are doing with big data technology. Thank you for coming on in a queue. Thank you.