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This is the Rough Cut version of the printed book.
Get Started Fast with Apache Hadoop® 2, YARN, and Today’s Hadoop Ecosystem
With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models.
Hadoop® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it.
Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more.
This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist.
Coverage Includes
Foreword
Preface
Chapter 1: Background and Concepts
Chapter 2: Installation Recipes
Chapter 3: Hadoop Distributed File System Basics
Chapter 4: Running Example Programs and Benchmarks
Chapter 5: Hadoop MapReduce Framework
Chapter 6: MapReduce Programming
Chapter 7: Essential Hadoop Tools
Chapter 8: Hadoop YARN Applications
Chapter 9: Managing Hadoop with Apache Ambari
Chapter 10: Basic Hadoop Administration Procedures
Appendix A: Book Webpage and Code Download
Appendix B: Getting Started Flow Chart and Trouble Shooting Guide
Appendix C: Summary of Apache Hadoop Resources by Topic
Appendix D: Installing and Using Apache HUE
Appendix E: Installing and Using Apache Spark
Index