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.
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.
- 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
Chapter 1: Hadoop in a heartbeat
Chapter 2: Introduction to YARN
Part 2: Data logistics
Chapter 3: Data serialization— working with text and beyond
Chapter 4: Organizing and optimizing data in HDFS
Chapter 5: Moving data into and out of Hadoop
Part 3: Big data patterns
Chapter 6: Applying MapReduce patterns to big data
Chapter 7: Utilizing data structures and algorithms at scale
Chapter 8: Tuning, debugging, and testing
Part 4: Beyond MapReduce
Chapter 9: SQL on Hadoop
Chapter 10: Writing a YARN application
Appendix: Installing Hadoop and friends
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