Book description
Pro Apache Hadoop, Second Edition brings you up to speed on Hadoop the framework of big data. Revised to cover Hadoop 2.0, the book covers the very latest developments such as YARN (aka MapReduce 2.0), new HDFS high-availability features, and increased scalability in the form of HDFS Federations. All the old content has been revised too, giving the latest on the ins and outs of MapReduce, cluster design, the Hadoop Distributed File System, and more.
This book covers everything you need to build your first Hadoop cluster and begin analyzing and deriving value from your business and scientific data. Learn to solve big-data problems the MapReduce way, by breaking a big problem into chunks and creating small-scale solutions that can be flung across thousands upon thousands of nodes to analyze large data volumes in a short amount of wall-clock time. Learn how to let Hadoop take care of distributing and parallelizing your softwareyou just focus on the code; Hadoop takes care of the rest.
Table of contents
- Cover
- Title
- Copyright
- Dedication
- Contents at a Glance
- Contents
- About the Authors
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Chapter 1 : Motivation for Big Data
- Chapter 2 : Hadoop Concepts
- Chapter 3 : Getting Started with the Hadoop Framework
- Chapter 4 : Hadoop Administration
-
Chapter 5 : Basics of MapReduce Development
- Hadoop and Data Processing
- Reviewing the Airline Dataset
-
MapReduce Programming Patterns
- Map-Only Jobs (SELECT and WHERE Queries)
- Problem Definition: SELECT Clause
- Problem Definition: WHERE Clause
- Map and Reduce Jobs (Aggregation Queries)
- Problem Definition: GROUP BY and SUM Clauses
- Improving Aggregation Performance Using the Combiner
- Problem Definition: Optimized Aggregators
- Role of the Partitioner
- Problem Definition: Split Airline Data by Month
- Bringing it All Together
- Summary
- Chapter 6 : Advanced MapReduce Development
- Chapter 7 : Hadoop Input/Output
- Chapter 8 : Testing Hadoop Programs
- Chapter 9 : Monitoring Hadoop
- Chapter 10 : Data Warehousing Using Hadoop
- Chapter 11 : Data Processing Using Pig
- Chapter 12 : HCatalog and Hadoop in the Enterprise
- Chapter 13 : Log Analysis Using Hadoop
-
Chapter 14 : Building Real-Time Systems Using HBase
- What Is HBase?
- Typical HBase Use-Case Scenarios
- HBase Data Model
-
HBase Commands and APIs
- Getting a Command List: help Command
- Creating a Table: create Command
- Adding Rows to a Table: put Command
- Retrieving Rows from the Table: get Command
- Reading Multiple Rows: scan Command
- Counting the Rows in the Table: count Command
- Deleting Rows: delete Command
- Truncating a Table: truncate Command
- Dropping a Table: drop Command
- Altering a Table: alter Command
- HBase Architecture
- HBase Configuration: An Overview
- HBase Application Design
- HBase Operations Using Java API
- HBase MapReduce Integration
- A MapReduce Job to Read an HBase Table
- HBase and MapReduce Clusters
- Summary
- Chapter 15 : Data Science with Hadoop
- Chapter 16 : Hadoop in the Cloud
- Chapter 17 : Building a YARN Application
- Appendix A: Installing Hadoop
- Appendix B: Using Maven with Eclipse
- Appendix C: Apache Ambari
- Index
Product information
- Title: Pro Apache Hadoop, Second Edition
- Author(s):
- Release date: September 2014
- Publisher(s): Apress
- ISBN: 9781430248644
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