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Apache Hadoop 3 Quick Start Guide

You're reading from Apache Hadoop 3 Quick Start Guide Learn about big data processing and analytics

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788999830

Length 220 pages

Edition 1st Edition

Languages

Java

Tools

Hadoop

Concepts

Big Data

Author (1):

Hrishikesh Vijay Karambelkar

View More author details

Table of Contents (10) Chapters

Preface

1. Hadoop 3.0 - Background and Introduction

2. Planning and Setting Up Hadoop Clusters FREE CHAPTER

3. Deep Dive into the Hadoop Distributed File System

4. Developing MapReduce Applications

5. Building Rich YARN Applications

6. Monitoring and Administration of a Hadoop Cluster

7. Demystifying Hadoop Ecosystem Components

8. Advanced Topics in Apache Hadoop

9. Other Books You May Enjoy

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Technical requirements

You will need Eclipse development environment and Java 8 installed on your system where you can run/tweak these examples. If you prefer to use maven, then you will need maven installed to compile the code. To run the example, you also need Apache Hadoop 3.1 setup on Linux system. Finally, to use the Git repository of this book, you need to install Git.

The code files of this chapter can be found on GitHub:
https://github.com/PacktPublishing/Apache-Hadoop-3-Quick-Start-Guide/tree/master/Chapter8

Check out the following video to see the code in action:

http://bit.ly/2qiETfO

The rest of the chapter is locked

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Authors (1)

Vijay Karambelkar

Vijay Karambelkar

Hrishikesh Vijay Karambelkar is an innovator and an enterprise architect with 16 years of software design and development experience, specifically in the areas of big data, enterprise search, data analytics, text mining, and databases. He is passionate about architecting new software implementations for the next generation of software solutions for various industries, including oil and gas, chemicals, manufacturing, utilities, healthcare, and government infrastructure. In the past, he has authored three books for Packt Publishing: two editions of Scaling Big Data with Hadoop and Solr and one of Scaling Apache Solr. He has also worked with graph databases, and some of his work has been published at international conferences such as VLDB and ICDE.

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