 What else are we seeing? So let's talk about the software. So one of the things you were talking about that I really liked, you were going down the list. So on Mike's slide, he had all the new features around the core. You just go down the core and rattle off your version of what it means and what it is. So you start off with, say, HBase. We talked about that already. What are the other ones that are out there? So the projects that we have right now? The projects that are around, those tools that are to be built. Yeah, so the foundational one, as we mentioned before, is HDFS for storage, map with use for processing. And then the immediate layer above that is how to make map with use easier for the masses. So not everybody knows how to learn map with use Java. Everybody knows SQL, right? So one of the most successful projects right now that has the highest attached rate, meaning people usually when they install Hadoop and install it as well, is Hive. So Hive takes SQL, and to Jeff Hammerbacker, my co-founder, when he was at Facebook, his team built the Hive system. Essentially, Hive takes SQL, so you don't have to learn a new language. You already know SQL. And then converts that into map with use for you. That not only expands the developer base for how many people can use Hadoop, but also makes it easier to integrate Hadoop through ODBC and JDBC, integrated with BI tools like MicroSherry and Tableau and Informatica, et cetera, et cetera. He mentioned R, too. He mentioned R programming. R as well. Yeah, R is one of our best partnerships. We're very, very happy with them. So that's one of the very key projects is Hive. A sister project to Hive is called PIG. PIG Latin is a language that Yahoo invented that you have to learn the language, but it's very easy to learn compared to map with use. But once you learn it, you can specify very deep data pipelines. SQL is good for queries. It's not good for data pipelines because it becomes very convoluted. It becomes very hard for the human brain to understand it. So PIG is much more natural to the human brain. It's more like Perl, very similar to Perl scripting kind of languages. So with PIG, you can write very, very long data pipelines. Again, very successful projects doing very, very well. Another key project is HBase, like you said. So HBase allows you to do low latency. So you can do very, very quick lookups and also allows you to do transactions. So you can do updates, inserts, and deletes. So one of the talks here at Hadoop World, which I recommend people watch when the videos come out, is the talk by Jonathan Gray from Facebook. And he talked about how they use HBase. He's running on here in the cube later. Yeah, so you should grill him on that. So they use HBase now for many, many things within Facebook. They have a big team now committed to building and improving HBase with us and with the community at large. And they're using it for doing their online messaging system, the live mail system in Facebook. It's powered by HBase right now. Again, eBay, the Cassini project, they gave a keynote earlier today at the conference as well, is using HBase as well. So HBase is definitely one of the projects that's growing very, very quickly right now within the Hadoop ecosystem. Another key project that Jeff alluded to earlier when he was on here is Flume. So Flume is very instrumental because you have this nice system Hadoop. But Hadoop is useless unless you have data inside it. So how do you get the data inside Hadoop? So Flume essentially is this very nice framework for having these agents all over your infrastructure inside your web servers, inside your application servers, inside your mobile devices, your network equipment that collects all of that data and then reliably and materializes it inside Hadoop. So Flume does that. Another good project is Uzi. There's so many of them. I don't know how long you want me to keep going here. But Uzi is a workflow processing system. So Uzi allows you to define a series of jobs, some of them in PIG, some of them in Hive, some of them in MapRedUse. You can define a series of them and then link them to each other and say, only start this job when these other jobs finish because I'm waiting for the input from them before I can kick off and so on. So Uzi is a very nice framework that will do that. We manage the whole graph of jobs for you and retry things when they fail, et cetera, et cetera. Another good project is WIR, W-H-I-R-R. And WIR allows you to very easily start the Hadoop cluster on top of Amazon EC2 and top of Rackspace. It's more for kicking off, it's for kicking off Hadoop instances or HBASE instances on any virtual infrastructure. VMware vCloud, so it supports all of the major virtualized infrastructure systems out there. Eucalyptus as well and so on. So that's where W-H-I-R-R. Avru is another key project. It's DuckCutting's main project right now. None of DuckCutting came on stage yet with you guys. So Avru is a project about how do we encode with our files the schema of these files, right? Because when you open up a text file and you don't know what the columns mean and how to parse it, it becomes very hard to work with it. So Avru allows you to do that much more easily. It's also useful for doing RPC, we call RPC, remove procedure calls for having different services talk to each other. Avru is very useful for that as well. And the list keeps going on on Scoop. Mahalt, yeah, which we just add thanks for mentioning for reminding me of Mahalt. We just added Mahalt very recently. What is that? I'm not familiar with it. So Mahalt is a data mining library. So Mahalt takes some of the most popular data mining algorithms for doing clustering and regression and statistical modeling and implements them using the MapReduce model. Does it have machine learning in it too? Yes, yes. So that's the machine learning. So yes, state vector machines and so on. What's Scoop? So Scoop, you know all of them. Thanks for feeding me all the names. The ones I don't understand. There's so many of them right now. I can't even remember all of them. So Scoop actually is a very interesting project. It's short for SQL to Hadoop. And hence the name Scoop, right? So SQ from SQL and OOP from Hadoop. And it also means Scoop as in scooping up stuff when you scoop up ice cream. And the idea for Scoop is to make it easy to move data between relational systems like Oracle, Teradata, and ITS, Vertica, and so on, and Hadoop. So you can very simply say Scoop, the name of the table inside the relational system, the name of the file inside Hadoop, and the table will be copied over to the file. And vice versa, you can say Scoop, the name of the file in Hadoop, the name of the table over there, it will move the table over there. So it's a connectivity tool between the relational world and the Hadoop world. Great, great tutorial. And all of these are Apache products. They're all projects built within the Apache. Okay, this is not part of your...