Hadoop in Action

Author(s): Chuck Lam

Year (published): 2010-12-01T00:00:00.000-0800

Pages: 325

Abstract: Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs.

Introduction

Hadoop is an open source framework implementing the MapReduce algorithm behind Google's approach to querying the distributed data sets that constitute the internet. This definition naturally leads to an obvious question, "What are "maps" and why do they need to be "reduced " Massive data sets can be extremely difficult to analyze and query using traditional mechanisms, especially when the queries themselves are quite complicated. In effect, the MapReduce algorithm breaks up both the query and the data set into constituent parts that's the "mapping." The mapped components of the query can be processed simultaneously or "reduced" to rapidly return results. Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs. The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework. This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples.

Data from https://github.com/bvaughn/infinite-list-reflow-examples/blob/master/books.json