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Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem

Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem

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  • Copyright 2016
  • Dimensions: 7" x 9-1/8"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-405000-2
  • ISBN-13: 978-0-13-405000-3

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

  • Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce
  • Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses
  • Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters
  • Exploring the Hadoop Distributed File System (HDFS)
  • Understanding the essentials of MapReduce and YARN application programming
  • Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase
  • Observing application progress, controlling jobs, and managing workflows
  • Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration
  • Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark

Sample Content

Table of Contents

Foreword         xi

Preface          xiii

Acknowledgments         xix

About the Author          xxi

Chapter 1: Background and Concepts         1

Defining Apache Hadoop  1

A Brief History of Apache Hadoop  3

Defining Big Data  4

Hadoop as a Data Lake  5

Using Hadoop: Administrator, User, or Both  6

First There Was MapReduce  7

Moving Beyond MapReduce with Hadoop V2   13

The Apache Hadoop Project Ecosystem   15

Summary and Additional Resources   18

Chapter 2: Installation Recipes         19

Core Hadoop Services   19

Planning Your Resources   21

Installing on a Desktop or Laptop   23

Installing Hadoop with Ambari   40

Installing Hadoop in the Cloud Using Apache Whirr   56

Summary and Additional Resources   62

Chapter 3: Hadoop Distributed File System Basics          63

Hadoop Distributed File System Design Features   63

HDFS Components   64

HDFS User Commands   72

HDFS Web GUI   77

Using HDFS in Programs   77

Summary and Additional Resources   83

Chapter 4: Running Example Programs and Benchmarks          85

Running MapReduce Examples   85

Running Basic Hadoop Benchmarks   95

Summary and Additional Resources   98

Chapter 5: Hadoop MapReduce Framework         101

The MapReduce Model   101

MapReduce Parallel Data Flow   104

Fault Tolerance and Speculative Execution   107

Summary and Additional Resources   109

Chapter 6: MapReduce Programming          111

Compiling and Running the Hadoop WordCount Example   111

Using the Streaming Interface   116

Using the Pipes Interface   119

Compiling and Running the Hadoop Grep Chaining Example   121

Debugging MapReduce   124

Summary and Additional Resources   128

Chapter 7: Essential Hadoop Tools         131

Using Apache Pig   131

Using Apache Hive   134

Using Apache Sqoop to Acquire Relational Data   139

Using Apache Flume to Acquire Data Streams   148

Manage Hadoop Workflows with Apache Oozie   154

Using Apache HBase   163

Summary and Additional Resources   169

Chapter 8: Hadoop YARN Applications          171

YARN Distributed-Shell   171

Using the YARN Distributed-Shell   172

Structure of YARN Applications   178

YARN Application Frameworks   179

Summary and Additional Resources   184

Chapter 9: Managing Hadoop with Apache Ambari          185

Quick Tour of Apache Ambari   186

Managing Hadoop Services   194

Changing Hadoop Properties   198

Summary and Additional Resources   204

Chapter 10: Basic Hadoop Administration Procedures           205

Basic Hadoop YARN Administration   206

Basic HDFS Administration   208

Capacity Scheduler Background   220

Hadoop Version 2 MapReduce Compatibility   222

Summary and Additional Resources   225

Appendix A: Book Webpage and Code Download          227

Appendix B: Getting Started Flowchart and Troubleshooting Guide         229

Getting Started Flowchart   229

General Hadoop Troubleshooting Guide   229

Appendix C: Summary of Apache Hadoop Resources by Topic          243

General Hadoop Information   243

Hadoop Installation Recipes   243

HDFS   244

Examples   244

MapReduce   245

MapReduce Programming   245

Essential Tools   245

YARN Application Frameworks   246

Ambari Administration   246

Basic Hadoop Administration   247

Appendix D: Installing the Hue Hadoop GUI         249

Hue Installation   249

Starting Hue   253

Hue User Interface   253

Appendix E: Installing Apache Spark         257

Spark Installation on a Cluster   257

Starting Spark across the Cluster   258

Installing and Starting Spark on the Pseudo-distributed Single-Node Installation   260

Run Spark Examples   260

Index         261


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