<DIV><p>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.</p><p>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.</p><ul><li><b>Summarization patterns:</b> get a top-level view by summarizing and grouping data </li><li><b>Filtering patterns:</b> view data subsets such as records generated from one user </li><li><b>Data organization patterns:</b> reorganize data to work with other systems, or to make MapReduce analysis easier </li><li><b>Join patterns:</b> analyze different datasets together to discover interesting relationships </li><li><b>Metapatterns:</b> piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job </li><li><b>Input and output patterns:</b> customize the way you use Hadoop to load or store data </li></ul><p>"A clear exposition of MapReduce programs for common data processing patterns—this book is indespensible for anyone using Hadoop."<br><br> --Tom White, author of <i>Hadoop: The Definitive Guide</i></p></div>