 So, I'm Clint Finley. I'm here with Mike. Can you introduce yourself? I'm Mike Finley. I'm the vice president of strategy for Riley Mead. I've been editing books and doing all sorts of stuff there for like 20 years. Okay. How long have you been doing, covering three or four years now? So, pretty much since the term. Pretty much since the term. Although I was, when I wrote, when I wrote What Is Data Science a couple of years ago, I was surprised to find out that Tim O'Reilly was talking about big data back in 2001. Oh, wow. So, which is a little bit humbling. Okay. So, what's your take on how things have been going at this event? How is big data can do, in particular, evolving? Is it ready to move on time? Yeah. Definitely ready for fine time. So, some of the other things you may not think quite in writing what you're getting there, but I've heard a fair number of people talking about trials and you know, in the case of running. But that's all growing pains. And the more people here on it, the faster it's going to be. And I think it's wonderful. Okay. I'm sort of wondering whether the ecosystem is coming to both the best thing for the worst thing about it and that there's all these different rules that you might need to use in order to make effective use of it. And so, they're looking at some of the alternatives. You know, their proposition is, God, the whole need act, the whole need act might be in one integrated act. It doesn't have to kind of fill out. They can choose from five and all these other rules. What's your take on that? Interesting question. I like the ability to hold what we need from what you're saying. I think building up the system is, having said that I'm not that familiar with what I think is being looked at in office. But I like the origins of the classic small tools. They'll do one thing. Small pieces will be joined. And now, while very few in the middle of the system would qualify as small, I think they do, I think they didn't fit that model where you have small pieces or you have pieces that are used. And they're going to say, you might want to buy a small piece again. But a simple model of the project. You know anything about Big Top? No, I just heard about that this morning. But that sounds really important. I really like the idea of having a model that exists. Primarily, it has an added ability to integrate to the thing that you're talking about. That strikes me as a good start forward. Is there anything that you've been hearing from the customers about how small is their world? Are people starting to use it? Are people still using water? I've talked to more developers than the customers. Even on very small customers, they have customers talking to somebody, talking to their heads, and I think actually that's an interesting area to look at. Small customers are only running, and that's only running a dozen weeks on those. It's entirely two weeks. People have 100, 500,000, but I think a big broker is going to be just getting into it. I hosted Amazon's Elastic Map Produce. I know lots of people who are working on it. I think they do a great job of minimizing getting started. As you know, one of the sub-spots is getting all the different buyers together. While EMR does not make that problem go away, it simplifies it a lot. What are some of the more interesting uses that you've been seeing? What are you doing? Have you seen anything about everything? Actually, I have. Well, I mean, people are doing this, sort of predicting engines, scene learning, with artificial intelligence. Pretty much all the classes. I've heard about it being used in financial services. I've been hearing mixed things about it. Yeah, actually. We interviewed him earlier on the queue today. We've talked a lot about it. It seems like it's ideal for the financial industry. I think it's a problem there. It has to overcome. That can be a data box in an IT investment. Well, as the second team on the first day, the key thing to be interested in here is to figure out how to build the financial industry. All right, well, that's about all the time we have. We're going to go back to the cube desk. Thanks again for your time, Mike.