Title: Learning Hadoop 2
Author: Gabriele Modena, Garry Turkington
Length: 316 pages
Publisher: Packt Publishing
Publication Date: 2014-12-29
Design and implement data processing, lifecycle
management, and analytic workflows with the cutting-edge toolbox of Hadoop 2
About This Book
Construct state-of-the-art applications using higher-level interfaces and tools beyond the traditional MapReduce approach
Use the unique features of Hadoop 2 to model and analyze Twitter's global stream of user generated data
Develop a prototype on a local cluster and deploy to the cloud (Amazon Web Services)
Who This Book Is For
If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.
This book introduces you to the world of building data-processing applications with the wide variety of tools supported by Hadoop 2. Starting with the core components of the framework?HDFS and YARN?this book will guide you through how to build applications using a variety of approaches.
You will learn how YARN completely changes the relationship between MapReduce and Hadoop and allows the latter to support more varied processing approaches and a broader array of applications. These include real-time processing with Apache Samza and iterative computation with Apache Spark. Next up, we discuss Apache Pig and the dataflow data model it provides. You will discover how to use Pig to analyze a Twitter dataset.
With this book, you will be able to make your life easier by using tools such as Apache Hive, Apache Oozie, Hadoop Streaming, Apache Crunch, and Kite SDK. The last part of this book discusses the likely future direction of major Hadoop components and how to get involved with the Hadoop community.
Table of Contents
Chapter 1. Introduction
Chapter 2. Storage
Chapter 3. Processing – Mapreduce And Beyond
Chapter 4. Real-Time Computation With Samza
Chapter 5. Iterative Computation With Spark
Chapter 6. Data Analysis With Apache Pig
Chapter 7. Hadoop And Sql
Chapter 8. Data Lifecycle Management
Chapter 9. Making Development Easier
Chapter 10. Running A Hadoop Cluster
Chapter 11. Where To Go Next