The Best MapReduce Books of All Time

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 "A clear exposition of MapReduce programs for common data processing patterns―this book is indespensible for anyone using Hadoop.". --Tom White, author of Hadoop: The Definitive Guide.

Our world is being revolutionized by data-driven access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers.

The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks.

Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets. About This BookProcess large and complex datasets using next generation HadoopInstall, configure, and administer MapReduce programs and learn what's new in MapReduce v2More than 90 Hadoop MapReduce recipes presented in a simple and straightforward manner, with step-by-step instructions and real-world examplesWho This Book Is ForIf you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce.

This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux. What You Will LearnConfigure and administer Hadoop YARN, MapReduce v2, and HDFS clustersUse Hive, HBase, Pig, Mahout, and Nutch with Hadoop v2 to solve your big data problems easily and effectivelySolve large-scale analytics problems using MapReduce-based applicationsTackle complex problems such as classifications, finding relationships, online marketing, recommendations, and searching using Hadoop MapReduce and other related projectsPerform massive text data processing using Hadoop MapReduce and other related projectsDeploy your clusters to cloud environmentsIn DetailStarting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments. Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results.