What this book covers
Chapter 1, Getting Started with Hadoop 2.x, introduces you to the installation details needed for single and multi-node Hadoop clusters. It also contains the recipes that will help you understand various important cluster management techniques, such as decommissioning, benchmarking, and so on.
Chapter 2, Exploring HDFS, provides you with hands-on recipes to manage and maintain the Hadoop Distributed File System (HDFS) in an efficient way. You will learn some important practices, such as transient encryption, saving data in a compressed format, recycling deleted data from HDFS, and so on.
Chapter 3, Mastering Map Reduce Programs, enlightens you about very important recipes for Map Reduce programming, which take you beyond the simple Word Count program. You will learn about various customization techniques in detail.
Chapter 4, Data Analysis Using Hive, Pig, and Hbase, takes you to the analytical world of Hive, Pig, and Hbase. This chapter talks about the use of various file formats, such as RC, ORC, Parquet, and so on. You will also get introduced to the Hbase NoSQL database.
Chapter 5, Advanced Data Analysis Using Hive, provides insights on the usage of serializers and deserializers (SerDe) in Hive for JSON and XML data operations. This chapter will provide you with a detailed explanation for Twitter sentiment analysis using Hive.
Chapter 6, Data Import/Export Using Sqoop and Flume, covers various recipes to import and export data from sources, such as RDBMS, Kafka, web log servers, and so on, using Sqoop and Flume.
Chapter 7, Automation of Hadoop Tasks Using Oozie, introduces you to a very rich scheduling tool called Oozie, which will help you build automated production-ready Big Data applications.
Chapter 8, Machine Learning and Predictive Analytics Using Mahout and R, gives you an end-to-end implementation of predictive analytics applications using Mahout and R. It covers the various visualization options available in R as well.
Chapter 9, Integration with Apache Spark, introduces you to a very important distributed computing framework called Apache Spark. It covers basic to advanced topics such as installation, Spark application development and execution, usage of the Spark Machine Learning Library, MLib, and graph processing using Spark.
Chapter 10, Hadoop Use Cases, provides you with end-to-end implementations of Hadoop use cases from various domains, such as telecom, finance, e-commerce, and so on.
