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Bibliographic Details

Title: Hadoop MapReduce Cookbook
Publisher: Shroff Publishers & Distributors Pvt Ltd
Publication Date: 2013
Language: English
ISBN 10: 9351102203
ISBN 13: 9789351102205
Binding: Soft cover
Condition: good

About this title

Synopsis

Learn how to use Hadoop MapReduce to analyze large and complex datasets with this comprehensive cookbook. Over fifty recipes with step-by-step instructions quickly take your Hadoop skills to the next level.

Overview

Learn to process large and complex data sets, starting simply, then diving in deep
Solve complex big data problems such as classifications, finding relationships, online marketing and recommendations.
More than 50 Hadoop MapReduce recipes, presented in a simple and straightforward manner, with step-by-step instructions and real world examples.

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.

What you will learn from this book

How to install Hadoop MapReduce and HDFS to begin running examples
How to configure and administer Hadoop and HDFS securely
Understanding the internals of Hadoop and how Hadoop can be extended to suit your needs
How to use HBase, Hive, Pig, Mahout, and Nutch to get things done easily and efficiently
How to use MapReduce to solve many types of analytics problems
Solve complex problems such as classifications, finding relationships, online marketing, and recommendations
Using MapReduce for massive text data processing
How to use cloud environments to perform Hadoop computation

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 written 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.

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.

"About this title" may belong to another edition of this title.

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