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Hadoop in Practice: Includes 104 Techniques
Publication Date: 2014
Number of pages: 512
Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Book
It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available.
Readers need to know a programming language like Java and have basic familiarity with Hadoop.
What's InsideThoroughly updated for Hadoop 2How to write YARN applicationsIntegrate real-time technologies like Storm, Impala, and SparkPredictive analytics using Mahout and RR Readers need to know a programming language like Java and have basic familiarity with Hadoop.
About the Author
Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects.
Table of ContentsPART 1 BACKGROUND AND FUNDAMENTALSHadoop in a heartbeatIntroduction to YARNPART 2 DATA LOGISTICSData serialization—working with text and beyondOrganizing and optimizing data in HDFSMoving data into and out of HadoopPART 3 BIG DATA PATTERNSApplying MapReduce patterns to big dataUtilizing data structures and algorithms at scaleTuning, debugging, and testingPART 4 BEYOND MAPREDUCESQL on Hadoop Writing a YARN application