Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you’re using.

Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop.

Summarization patterns: get a top-level view by summarizing and grouping data
Filtering patterns: view data subsets such as records generated from one user
Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier
Join patterns: analyze different datasets together to discover interesting relationships
Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job
Input and output patterns: customize the way you use Hadoop to load or store data