Hadoop in Practice, 2nd Edition

Hadoop in Practice, 2nd Edition

2014 | ISBN: 1617292222 | English | 512 pages | PDF + EPUB | 9 MB + 12 MB

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.

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

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