Every industry is dealing with more data every day. The data comes from more and more devices and we need to both store and process the data efficiently. We’ll see how Apache Hadoop MapReduce works and scales to process these vast quantities of data.

Working with software libraries saves time and effort, and this is especially true with distributed computing systems like Hadoop. However, learning the underlying concepts and API takes time, and that often holds teams back. Jesse’s novel approach to MapReduce uses playing cards to illustrate the workflow in a simple, understandable way. This allows you to move physical objects while learning the concepts behind MapReduce. Then we’ll move from conceptual to practical and write code to do the same thing using Hadoop. We’ll work through several examples in several programming languages to ensure you have the knowledge you need to use MapReduce on your next project.

This series of screencasts is a focused look at how MapReduce works and the APIs behind it. Although Hadoop is written in Java, we’ll see how to use it with any language. Jesse teaches using examples in Java, Ruby, Python and Perl.