Hadoop in Practice

Summary

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 Inside

• Thoroughly updated for Hadoop 2
• How to write YARN applications
• Integrate real-time technologies like Storm, Impala, and Spark
• Predictive 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 Contents

PART 1 BACKGROUND AND FUNDAMENTALS
• Hadoop in a heartbeat
• Introduction to YARN

PART 2 DATA LOGISTICS
• Data serialization—working with text and beyond
• Organizing and optimizing data in HDFS
• Moving data into and out of Hadoop

PART 3 BIG DATA PATTERNS
• Applying MapReduce patterns to big data
• Utilizing data structures and algorithms at scale
• Tuning, debugging, and testing

PART 4 BEYOND MAPREDUCE
• SQL on Hadoop
• Writing a YARN application