Hadoop in practice: includes 104 techniques 2nd edition, alex holmes

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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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