What this book covers

Chapter 1, Getting Started with Hadoop v2, introduces Hadoop MapReduce, YARN, and HDFS, and walks through the installation of Hadoop v2.

Chapter 2, Cloud Deployments – Using Hadoop Yarn on Cloud Environments, explains how to use Amazon Elastic MapReduce (EMR) and Apache Whirr to deploy and execute Hadoop MapReduce, Pig, Hive, and HBase computations on cloud infrastructures.

Chapter 3, Hadoop Essentials – Configurations, Unit Tests, and Other APIs, introduces basic Hadoop YARN and HDFS configurations, HDFS Java API, and unit testing methods for MapReduce applications.

Chapter 4, Developing Complex Hadoop MapReduce Applications, introduces you to several advanced Hadoop MapReduce features that will help you develop highly customized and efficient MapReduce applications.

Chapter 5, Analytics, explains how to perform basic data analytic operations using Hadoop MapReduce.

Chapter 6, Hadoop Ecosystem – Apache Hive, introduces Apache Hive, which provides data warehouse capabilities on top of Hadoop, using a SQL-like query language.

Chapter 7, Hadoop Ecosystem II – Pig, HBase, Mahout, and Sqoop, introduces the Apache Pig data flow style data-processing language, Apache HBase NoSQL data storage, Apache Mahout machine learning and data-mining toolkit, and Apache Sqoop bulk data transfer utility to transfer data between Hadoop and the relational databases.

Chapter 8, Searching and Indexing, introduces several tools and techniques that you can use with Apache Hadoop to perform large-scale searching and indexing.

Chapter 9, Classifications, Recommendations, and Finding Relationships, explains how to implement complex algorithms such as classifications, recommendations, and finding relationships using Hadoop.

Chapter 10, Mass Text Data Processing, explains how to use Hadoop and Mahout to process large text datasets and how to perform data preprocessing and loading of operations using Hadoop.