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In Detail
We are facing an avalanche of data. The unstructured data we gather can contain many insights that might hold the key to business success or failure. Harnessing the ability to analyze and process this data with Hadoop MapReduce is one of the most highly sought after skills in today's job market.
"Hadoop MapReduce Cookbook" is a one-stop guide to processing large and complex data sets using the Hadoop ecosystem. The book introduces you to simple examples and then dives deep to solve in-depth big data use cases.
"Hadoop MapReduce Cookbook" presents more than 50 ready-to-use Hadoop MapReduce recipes in a simple and straightforward manner, with step-by-step instructions and real world examples.
Start with how to install, then configure, extend, and administer Hadoop. Then write simple examples, learn MapReduce patterns, harness the Hadoop landscape, and finally jump to the cloud.
The book deals with many exciting topics such as setting up Hadoop security, using MapReduce to solve analytics, classifications, on-line marketing, recommendations, and searching use cases. You will learn how to harness components from the Hadoop ecosystem including HBase, Hadoop, Pig, and Mahout, then learn how to set up cloud environments to perform Hadoop MapReduce computations.
"Hadoop MapReduce Cookbook" teaches you how process large and complex data sets using real examples providing a comprehensive guide to get things done using Hadoop MapReduce.
Approach
Individual self-contained code recipes. Solve specific problems using individual recipes, or work through the book to develop your capabilities.
Who this book is for
If you are a big data enthusiast and striving to use Hadoop to solve your problems, this book is for you. Aimed at Java programmers with some knowledge of Hadoop MapReduce, this is also a comprehensive reference for developers and system admins who want to get up to speed using Hadoop.
- Sales Rank: #1367010 in eBooks
- Published on: 2013-01-25
- Released on: 2013-01-25
- Format: Kindle eBook
About the Author
Srinath Perera.
Srinath Perera is a Senior Software Architect at WSO2 Inc., where he overlooks the overall WSO2 platform architecture with the CTO. He also serves as a Research Scientist at Lanka Software Foundation and teaches as a visiting faculty at Department of Computer Science and Engineering, University of Moratuwa. He is a co-founder of Apache Axis2 open source project, and he has been involved with the Apache Web Service project since 2002, and is a member of Apache Software foundation and Apache Web Service project PMC. Srinath is also a committer of Apache open source projects Axis, Axis2, and Geronimo.
He received his Ph.D. and M.Sc. in Computer Sciences from Indiana University, Bloomington, USA and received his Bachelor of Science in Computer Science and Engineering from University of Moratuwa, Sri Lanka.
Srinath has authored many technical and peer reviewed research articles, and more detail can be found from his website. He is also a frequent speaker at technical venues.
He has worked with large-scale distributed systems for a long time. He closely works with Big Data technologies, such as Hadoop and Cassandra daily. He also teaches a parallel programming graduate class at University of Moratuwa, which is primarily based on Hadoop.
Thilina Gunarathne
Thilina Gunarathne is a Ph.D. candidate at the School of Informatics and Computing of Indiana University. He has extensive experience in using Apache Hadoop and related technologies for large-scale data intensive computations. His current work focuses on developing technologies to perform scalable and efficient large-scale data intensive computations on cloud environments.
Thilina has published many articles and peer reviewed research papers in the areas of distributed and parallel computing, including several papers on extending MapReduce model to perform efficient data mining and data analytics computations on clouds. Thilina is a regular presenter in both academic as well as industry settings.
Thilina has contributed to several open source projects at Apache Software Foundation as a committer and a PMC member since 2005. Before starting the graduate studies, Thilina worked as a Senior Software Engineer at WSO2 Inc., focusing on open source middleware development. Thilina received his B.Sc. in Computer Science and Engineering from University of Moratuwa, Sri Lanka, in 2006 and received his M.Sc. in Computer Science from Indiana University, Bloomington, in 2009. Thilina expects to receive his doctorate in the field of distributed and parallel computing in 2013.
Most helpful customer reviews
2 of 2 people found the following review helpful.
Good book, bad editing
By Doug Duncan
'Hadoop MapReduce Cookbook' by Srinath Perera and Thilina Gunarathne will help you out with getting things done in MapReduce. The content is pretty well thought out and the authors seem to have good knowledge on Hadoop and MapReduce. They start with the basics of setting up Hadoop in different configurations (local, pseudo-distributed and fully distributed) and how to work with HDFS, and then move out into the Hadoop ecosystem and how to use MapReduce with analytics.
While you will learn from this book, it appears the authors text suffered from bad editing. As the book progressed it seemed that the editing got more and more lax. There are numerous places in the book's text and code sections where spaces were missing so the words ran together. One with a basic understanding of Hadoop and MapReduce should be easily able to figure out what's going on, but a beginner will get tripped up and frustrated. I haven't looked at the downloadable code, but I will assume that it's fine and runnable, and with a book like this you will need the downloadable code as the text only discusses the relevant portions of the code.
I liked the book, but wished the editing hadn't gotten in the way of following along. With another round of editing I would have no problems raising my rating higher. Even with this, it's a good book and I would probably recommend it for someone trying to figure out how to use Hadoop MapReduce.
1 of 1 people found the following review helpful.
A good reference, but light on the logical details
By 08milluz
Hadoop MapReduce Cookbook is a good reference if you already have a decent understanding of Hadoop. It is helpful when you ask "how do I do that again?" or "what is the exact procedure for doing that?". The book does a decent job of explaining how to setup a cluster and basic cluster management, but it doesn't really explain the logic that is going on. While it is easy to understand what the author is doing, if you are new to Hadoop, you will likely get lost and have to reread some sections. This becomes more of an issue with more complex topics.
One of the strengths of the book is how it is broken down into recipes. Most programmers are able to read the code examples and understand what is going on without pages of explanation, and if that is you, the book will probably be very good. However, if you like lots of logical detail before you fully understand a new topic, you will probably find this book a bit lacking. The recipes are grouped logically and do follow a natural progression.
Overall a pretty good reference, but needs more details and explaination to be a good teaching book.
1 of 2 people found the following review helpful.
Already outdated
By D. Kidd
I think it would be better to follow online tutorials because this book is already rather outdated. Also, it didn't have enough background information for me. Having recipes just wasn't enough to get me started.
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