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Hadoop Beginner's Guide, by Garry Turkington
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Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services - just a willingness to learn the basics from this practical step-by-step tutorial.
Overview
- Learn tools and techniques that let you approach big data with relish and not fear.
- Shows how to build a complete infrastructure to handle your needs as your data grows.
- Hands-on examples in each chapter give the big picture while also giving direct experience.
In Detail
Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills.
"Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems.
Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems.
While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection .
In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.
What you will learn from this book
- The trends that led to Hadoop and cloud services, giving the background to know when to use the technology.
- Best practices for setup and configuration of Hadoop clusters, tailoring the system to the problem at hand
- Developing applications to run on Hadoop with examples in Java and Ruby
- How Amazon Web Services can be used to deliver a hosted Hadoop solution and how this differs from directly-managed environments
- Integration with relational databases, using Hive for SQL queries and Sqoop for data transfer
- How Flume can collect data from multiple sources and deliver it to Hadoop for processing
- What other projects and tools make up the broader Hadoop ecosystem and where to go next
Approach
As a Packt Beginner's Guide, the book is packed with clear step-by-step instructions for performing the most useful tasks, getting you up and running quickly, and learning by doing.
Who this book is written for
This book assumes no existing experience with Hadoop or cloud services. It assumes you have familiarity with a programming language such as Java or Ruby but gives you the needed background on the other topics.
- Sales Rank: #804347 in Books
- Published on: 2013-02-22
- Released on: 2013-02-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .90" w x 7.50" l, .0 pounds
- Binding: Paperback
- 398 pages
About the Author
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.
Most helpful customer reviews
24 of 24 people found the following review helpful.
It could have been a lot better.
By Amazon Customer
There are some things that are good about this book.
The explanations are pretty good.
But when someone is attempting to learn a system, one of the most frustrating things is to have a lot of errors in the examples.
If the author had just gone through trying to type in the examples to see if they worked they would have seen that at least 10% of the example commands are wrong and/or incomplete.
It wouldn't be so bad if I already knew Hadoop, the errors would be obvious because once I do google searches to figure out what the author meant to display, it is very obvious.
But I bought this book to learn the product which means, I'm left hanging every time there is an error.
I suggest you wait for the next version so maybe they will do some editing and get it right.
In the mean time, get a different book on Hadoop.
7 of 7 people found the following review helpful.
Helping the beginner getting started in Hadoop
By Doug Duncan
'Hadoop Beginner's Guide' by Gary Turkington is a book that helps walk beginners through understanding Hadoop and how to go about using it.
The first two chapters are introductory and cover what Hadoop is and how to install it. The second chapter also walks you through writing a couple basic MapReduce jobs.
Chapters 3, 4 and 5 take you deeper into MapReduce. It starts out where with small simple code and then goes on to more advanced topics such as joining different data sets.
Chapters 6 and 7 are on the administrative side and help you understand what to do when things start breaking and how to keep things running smoothly.
Chapters 8, 9 and 10 take you on a journey of some of the tools in the Hadoop ecosystem. Hive (chapter 8) and Sqoop (chapter 9) are tools that you will find yourself working with if you're more of a relational person or you need to connect Hadoop to your RDBMS. Flume (chapter 10) is a way for moving log data from remote servers into HDFS (among other things).
The last chapter talks about various things such as the different vendor distributions out there, other tools in the ecosystem and where you can find more information to continue on your journey.
As others have stated, there are some errors in the book's code. These are easily overcome by looking at the errata or a quick Google search. Sure it would be great if the book was perfect, but then I don't think that I've read a book that didn't have errors in it. Besides, trying to fix the errors that are presented, can sometimes make you learn more than you would have if everything was just copied and pasted. ;)
I would also have liked to see a chapter devoted to Pig as I feel that's just as important of an abstraction over MapReduce as Hive is, but I can understand that the ecosystem is too big to cover everything.
If you're new to Hadoop and you're willing to read the book and work through the examples, you will learn a lot.
7 of 7 people found the following review helpful.
Good description of Hadoop and its ecosystem
By Alexander Tarnowski
I read this book "out of context", meaning that I didn't have an interesting problem solvable by MapReduce at hand and a dire need to learn Hadoop at the time of reading. Instead, I took time to read this book with the purpose of determining whether it's a good beginner book or not. All in all, I'd say that it is. The author really succeeds in creating a context for Hadoop and its ecosystem.
From the second chapter and onwards, Hadoop is gradually introduced using very detailed instructions. The general format for doing this is by listing every single command the user needs to type and its output, so the book is full of terminal session listings. All such listings are followed by sections called "What just happened?" that explain in detail the purpose of the commands and their output. This is actually quite helpful for readers who understand what's happening from just looking at the session listing; such readers can safely skip these sections.
The above approach should enable any reader, regardless of level of experience, to follow along and do the exercises or labs, which is a good thing for a beginner book. I have a remark about this though: the session dumps could have been proofread better! I can't say that I read them through a magnifying glass, but still I found quite a few errors.
As for the contents, the book never shows the monster! In my opinion, the introductory chapter fails to actually establish a case for Hadoop and MapReduce. Yes, it's about big data, scaling and problems and so on, but I couldn't find a logical transition to Hadoop as a solution to these problems. Instead, chapter two illustrates the framework with a distributed calculation of pi and the word counting program (Hadoop's version of the "Hello world" program).
In a later chapter, Hadoop is used to process a dataset with UFO sightings, and then, in a chapter on advanced techniques a graph problem is solved. Not until that chapter did I start getting a feeling for what kind of problems Hadoop and MapReduce should be used for. This is what I mean by "never showing the monster". Being an introductory text, I'd prefer the first or second chapter to describe some problems that are good candidates for the MapReduce paradigm, illustrate one of them, and then show how a distributed computation would help.
That said, I may be off track here. This is a book on Hadoop, and not MapReduce in general, and I did say that I read it without having intricate MapReduce problems at hand. This is pretty much my only criticism. If a reader doesn't perceive this as a problem, then there's nothing to complain about. After reading the book, I feel that I have a very good feeling for what Hadoop does and what building blocks in its ecosystem to use.
One more thing... Here and there the book contains examples of how to use Amazon's EMR. This didn't feel awfully important to me, but it provides an even more solid explanation of how to apply the framework to bigger problems and how it can be used in a cloud environment.
To sum up: a good and comprehensive book on Hadoop that covers the framework and its ecosystem, verbose and easy to follow examples, and a structure that leaves the reader with a sense of getting the big picture.
Minuses: could devote some more pages to the MapReduce paradigm and have its sample listings proofread better.
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