Learning Hadoop: RAW

Book and eBook expected October 2014. Pre-order now!
Learning Hadoop: RAW
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Table of Contents
Sample Chapters
  • Discover the newest features of Hadoop, including YARN
  • Explore the tools and techniques that will help you get to grips with big data
  • Easy to read, step by step tutorials to lead you through every aspect of Hadoop

Book Details

Language : English
Paperback : 316 pages [ 235mm x 191mm ]
Release Date : October 2014
ISBN : 1783285516
ISBN 13 : 9781783285518
Author(s) : Garry Turkington, Gabriele Modena
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source, RAW books

Chapter Availability


Chapter Number Title Availability
1 Introduction
2 Storage IN THE BOOK
3 Computation IN THE BOOK
4 Near realtime processing of data IN THE BOOK
5 Making development easier
6 Keeping things running
JULY 2014
7 Data analysis with Apache Pig JULY 2014
8 Hadoop and SQL queries and interoperability JULY 2014
9 Workflow Orchestration with Oozie and data lifecycle management JULY 2014
10 Summary/ where to go next JULY 2014

Garry Turkington

Garry Turkington has 14 years of industry experience, most of which has been focused on the design and implementation of large-scale distributed systems. In his current roles as VP Data Engineering at Improve Digital and the company’s lead architect he is primarily responsible for the realization of systems that store, process, and extract value from the company’s large data volumes. Before joining Improve Digital he spent time at Amazon UK where he led several software development teams, building systems that process the Amazon catalog data for every item worldwide. Prior to this he spent a decade in various government positions in both the UK and USA. He has BSc and PhD degrees in computer science from the Queens University of Belfast in Northern Ireland and a MEng in Systems Engineering from Stevens Institute of Technology in the USA.

Gabriele Modena

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Frequently bought together

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Price for both: $45.30

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What you will learn from this book

  • Install and configure your Hadoop environment
  • Create and manage Hadoop clusters
  • Analyze data with Apache Pig
  • Import data into Hadoop using Sqoop
  • Build workflows using Oozie

In Detail

Hadoop is an Open-Source Apache distribution designed to handle large amounts of data in a quick and efficient way. The introduction of YARN with the newest release of Hadoop means that these processes are made even more effective when handling obscene amounts of data.

With its friendly but thorough approach, this is the perfect guide for anyone wanting to get to grips with the newest release of Hadoop. Leading you through how to build working systems, we unravel the mystery of YARN and reveal how your data can work for you.

Starting with essentials, we show you how to install and configure Hadoop before really diving in to getting your data working for you. From basic set up to developing applications and maintaining the system, this book gives you everything you need to get started using Hadoop. We also cover tools such as Hive, Pig and YARN to really get the most out of your Hadoop installation.


This book is currently available as a RAW (Read As we Write) book. A RAW book is an ebook, and this one is priced at 20% off the usual eBook price. Once you purchase the RAW book, you can immediately download the content of the book so far, and when new chapters become available, you will be notified, and  can download the new version of the book. When the book is published, you will receive the full, finished eBook.

If you like, you can preorder the print book at the same time as you purchase the RAW book at a significant discount.

Since a RAW book is an eBook, a RAW book is non returnable and non refundable.

Local taxes may apply to your eBook purchase.


Presented in a step by step tutorial, we'll lead you through every aspect of getting your Hadoop system up and running effectively.

Who this book is for

If you need to use Hadoop to handle large amounts of data, then this book is for you! No prior knowledge of Hadoop is assumed.

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