More NFL Posts

More NFL Posts

I have two more guest posts covering my NFL Play-by-Play project. The first is in  and there is a more technical explanation on Cloudera’s VISION blog.

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Processing Big Data with MapReduce

Processing Big Data with MapReduce

I am proud to announce my latest series of screencasts on Hadoop MapReduce. It’s published again by the good people at Pragmatic Programmers.  These screencasts are the best way for a beginner to learn about Hadoop, unless they’re sitting in my class at Cloudera University.

Here’s few links to get started after you’ve purchased the screencasts:

First, you want a way to run Hadoop, MapReduce and Eclipse.  There is a virtual machine that is set up and running with everything you need.  I have a mini-screencast showing how to use Eclipse and debug things.

Next, you’ll need the source code for the screencast.  The first and third episodes’ source code is here and the second episode’s source code is here.

Finally, you’ll need the dataset for the second episode.  It uses the Nasdaq daily stock prices from InfoChimps.

The focus of the screencast isn’t administration and installation.  This screencast is focused on the developer side of things.  The source code is written to run on the Cloudera QuickStart VM out of the box.

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Cloudera QuickStart VM and Eclipse

Cloudera QuickStart VM and Eclipse

One of the common questions I get from students and developers relates to IDEs and MapReduce.  How you create a MapReduce project in Eclipse and debug it?  I have created a short screencast showing you how.

Cloudera QuickStart VM

The Cloudera QuickStart VM lets developers get started with writing MapReduce code without having to worry about software installs and configuration.  Everything is installed and ready to go.  You can download the image type that corresponds to your preferred virtualization platform.

Eclipse is installed on the VM and there is a link on the desktop to start it.

MapReduce and Eclipse

You can run and debug MapReduce code in Eclipse just like any other Java program.  There are a few differences between running MapReduce in a distributed cluster and in an IDE like Eclipse.  When you run MapReduce code in Eclipse, Hadoop runs in a special mode called LocalJobRunner.  All of the Hadoop daemons are run in a single JVM (Java Virtual Machine) instead of several different JVMs.  Another difference is that all file paths default to local file paths and not HDFS file paths.

With those caveats in mind, you can start putting in your breakpoints and debug your MapReduce code like any other Java program.

If you want to clone the same Git project as I do in the screencast, you can find it here.  From the terminal type in:

git clone git@github.com:eljefe6a/UnoExample.git

The project will be cloned to the current directory as a subdirectory.

Note that creating Eclipse projects manually is the easy way to get started.  If you are going to have Hadoop as part of an automated build process, you will want to do this in Maven.  In Maven, you can create Eclipse projects.  This blog post tells you how.  If you want to compile Hadoop from source using Eclipse, this blog post shows how.

Conclusion

Whether you want to start writing some MapReduce code or debug existing code, the QuickStart VM will help you do it quickly and easily.  This screencast walks you through it and gets you coding on your favorite IDE.

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Guest Post on O’Reilly Programming Blog

Guest Post on O’Reilly Programming Blog

I’ve written a guest post for my OSCON talk that is published on the O’Reilly Programming Blog.  I talk about augmenting dataset and dealing with unstructured data.  See you at OSCON!

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Million Monkeys Interview on The Creators Project

Million Monkeys Interview on The Creators Project

I did another interview on The Million Monkeys Project for The Creators Project.  The piece talks about other creative uses of Shakespeare’s works.

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Interview in the RGJ

Interview in the RGJ

Here’s a interview I did for the Reno Gazette Journal.  As a bonus, you get to see me coding barefoot.

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