Videos tagged with MapReduce

The old OpenDNS Stats system was built when we were doing 1 billion queries a day and had far outlived its usefulness. Playing hot potato with load on overworked servers all struggling to keep up gets old after a while, doesn’t it? This gave me the opportunity to start from a blank slate and build the system we need to serve us at 8 billion queries a day and scale to 16 or 24 billion. We ...

Hadoop, the open-source implementation of MapReduce, provides unprecedented opportunities for both research and education. On the research side, academics are now able to explore problems at scale with modest resource investments, either with inexpensive commodity clusters or through utility computing services. On the education side, Hadoop provides a nice vehicle to teach students about large-...

Virtualization is currently being used in cloud computing environments and traditional IT environments to improve the flexibility and manageability of the computing infrastructure, and to enable the sharing of computing resources. This means that applications (such as database systems) are increasingly being run on virtual machines and using virtualized storage. The performance of an applicatio...

This tutorial is a screen cast that will show you exactly how to work with Pig. We'll walk through examples discussed in the Pig Introduction.

Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. This lecture includes a work-along exercise.

A screencast illustrating how to configure Eclipse for Hadoop Development.

This tutorial is a screen cast that will show you exactly how to work with Hive. We'll walk through importing data, creating tables, and making queries.

  Hive is a powerful data warehousing application built on top of Hadoop which allows you to use SQL to access your data. This lecture will give an overview of Hive and the query language. This lecture includes a work-along exercise. The next video is a screencast of a user performing this exercise.  

  After we've introduced you to the tools, it's time to learn how to use them efficiently. Algorithms designed for running on MapReduce look a little different than those you've written before. We'll introduce you to some widely-used algorithms, common idioms to use when designing your own, and techniques for implementing these in Java MapReduce and scripting languages via HadoopStreaming....

Learn how to get started writing programs against Hadoop's API.