Hadoop in Practice

Summary"Hadoop in Practice" collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy [...]
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Summary"Hadoop in Practice" collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design. As you work through the tasks, you'll find yourself growing more comfortable with Hadoop and at home in the world of big data.About the TechnologyHadoop is an open source MapReduce platform designed to query and analyze data distributed across large clusters. Especially effective for big data systems, Hadoop powers mission-critical software at Apple, eBay, LinkedIn, Yahoo, and Facebook. It offers developers handy ways to store, manage, and analyze data.About the Book"Hadoop in Practice" collects 85 battle-tested examples and presents them in a problem/solution format. It balances conceptual foundations with practical recipes for key problem areas like data ingress and egress, serialization, and LZO compression. You'll explore each technique step by step, learning how to build a specific solution along with the thinking that went into it. As a bonus, the book's examples create a well-structured and understandable codebase you can tweak to meet your own needs.This book assumes the reader knows the basics of Hadoop. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.What's InsideConceptual overview of Hadoop and MapReduce85 practical, tested techniquesReal problems, real solutionsHow to integrate MapReduce and RTable of ContentsPART 1 BACKGROUND AND FUNDAMENTALSHadoop in a heartbeatPART 2 DATA LOGISTICSMoving data in and out of HadoopData serialization?working with text and beyondPART 3 BIG DATA PATTERNSApplying MapReduce patterns to big dataStreamlining HDFS for big dataDiagnosing and tuning performance problemsPART 4 DATA SCIENCEUtilizing data structures and algorithmsIntegrating R and Hadoop for statistics and morePredictive analytics with MahoutPART 5 TAMING THE ELEPHANTHacking with HiveProgramming pipelines with PigCrunch and other technologiesTesting and debugging