What this book covers
Chapter 1, Hadoop 2.X, discusses the improvements in Hadoop 2.X in comparison to its predecessor generation.
Chapter 2, Advanced MapReduce, helps you understand the best practices and patterns for Hadoop MapReduce, with examples.
Chapter 3, Advanced Pig, discusses the advanced features of Pig, a framework to script MapReduce jobs on Hadoop.
Chapter 4, Advanced Hive, discusses the advanced features of a higher-level SQL abstraction on Hadoop MapReduce called Hive.
Chapter 5, Serialization and Hadoop I/O, discusses the IO capabilities in Hadoop. Specifically, this chapter covers the concepts of serialization and deserialization support and their necessity within Hadoop; Avro, an external serialization framework; data compression codecs available within Hadoop; their tradeoffs; and finally, the special file formats in Hadoop.
Chapter 6, YARN – Bringing Other Paradigms to Hadoop, discusses YARN (Yet Another Resource Negotiator), a new resource manager that has been included in Hadoop 2.X, and how it is generalizing the Hadoop platform to include other computing paradigms.
Chapter 7, Storm on YARN – Low Latency Processing in Hadoop, discusses the opposite paradigm, that is, moving data to the compute, and compares and contrasts it with batch processing systems such as MapReduce. It also discusses the Apache Storm framework and how to develop applications in Storm. Finally, you will learn how to install Storm on Hadoop 2.X with YARN.
Chapter 8, Hadoop on the Cloud, discusses the characteristics of cloud computing and Hadoop's Platform as a Service offering across cloud computing service providers. Further, it delves into Amazon's managed Hadoop services, also known as Elastic MapReduce (EMR) and looks into how to provision and run jobs on a Hadoop EMR cluster.
Chapter 9, HDFS Replacements, discusses the strengths and drawbacks of HDFS when compared to other file systems. The chapter also draws attention to Hadoop's support for Amazon's S3 cloud storage service. At the end, the chapter illustrates Hadoop HDFS extensibility features by implementing Hadoop's support for S3's native file system to extend Hadoop.
Chapter 10, HDFS Federation, discusses the advantages of HDFS Federation and its architecture. Block placement strategies, which are central to the success of HDFS in the MapReduce environment, are also discussed in the chapter.
Chapter 11, Hadoop Security, focuses on the security aspects of a Hadoop cluster. The main pillars of security are authentication, authorization, auditing, and data protection. We will look at Hadoop's features in each of these pillars.
Chapter 12, Analytics Using Hadoop, discusses higher-level analytic workflows, techniques such as machine learning, and their support in Hadoop. We take document analysis as an example to illustrate analytics using Pig on Hadoop.
Appendix, Hadoop for Microsoft Windows, explores Microsoft Window Operating System's native support for Hadoop that has been introduced in Hadoop 2.0. In this chapter, we look at how to build and deploy Hadoop on Microsoft Windows natively.
