Finding patterns in massive event streams can be difficult, but learning how to find them doesn’t have to be. This unique hands-on guide shows you how to solve this and many other problems in large-scale data processing with simple, fun, and elegant tools that leverage Apache Hadoop. You’ll gain a practical, actionable view of big data by working with real data and real problems.
Perfect for beginners, this book’s approach will also appeal to experienced practitioners who want to brush up on their skills. Part I explains how Hadoop and MapReduce work, while Part II covers many analytic patterns you can use to process any data. As you work through several exercises, you’ll also learn how to use Apache Pig to process data.
Learn the necessary mechanics of working with Hadoop, including how data and computation move around the cluster
Dive into map/reduce mechanics and build your first map/reduce job in Python
Understand how to run chains of map/reduce jobs in the form of Pig scripts
Use a real-world dataset—baseball performance statistics—throughout the book
Work with examples of several analytic patterns, and learn when and where you might use them
Table of Contents
Part I. Introduction: Theory and Tools
Chapter 1. Hadoop Basics
Chapter 2. MapReduce
Chapter 3. A Quick Look into Baseball
Chapter 4. Introduction to Pig
Part II. Tactics: Analytic Patterns
Chapter 5. Map-Only Operations
Chapter 6. Grouping Operations
Chapter 7. Joining Tables
Chapter 8. Ordering Operations
Chapter 9. Duplicate and Unique Records
