Optimize Hadoop to master MapReduce using Packt’s new Book and eBook

March 2014 | Open Source, Web Development

Packt is pleased to announce the release of Optimizing Hadoop for MapReduce by Kaled Tannir. This book details the Hadoop MapReduce job performance optimization process with a number of clear and practical steps, helping readers to fully utilize their cluster’s node resources. The book is 120 pages long and is competitively priced at $34.99, while the eBook and Kindle versions are available for $17.84.

About the author: Kaled Tannir has been working with computers since 1980. He began programming with the legendary Sinclair Zx81 before moving on to Commodore products. He has a Bachelor's degree in Electronics, a Master's degree in System Information Architectures, in which he graduated with a professional thesis, and completed his education with a Research Master's degree. He is a Microsoft Certified Solution Developer (MCSD) and has more than 20 years of technical experience leading the development and implementation of software solutions. He currently works as an independent IT consultant and has previously worked as an infrastructure engineer, senior developer, and enterprise/solution architect for many companies in France and Canada. You can e-mail him at contact@khaledtannir.net.

MapReduce is the distribution system that the Hadoop MapReduce engine uses to distribute work around a cluster by working in parallel on smaller data sets. It is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, inverted index construction, document clustering, machine learning, and statistical machine translation.

Optimizing Hadoop for MapReduce introduces readers to advanced MapReduce concepts and teaches them everything from identifying the factors that affect MapReduce job performance to tuning the MapReduce configuration. Through a number of clear and practical steps, this book will help readers to fully utilize their cluster’s node resources.

The following chapters are covered in the book:

Chapter 1: Understanding Hadoop MapReduce

Chapter 2: An Overview of the Hadoop Parameters

Chapter 3: Detecting System Bottlenecks

Chapter 4: Identifying Resource Weaknesses

Chapter 5: Enhancing Map and Reduce Tasks

Chapter 6: Optimizing MapReduce Tasks

Chapter 7: Best Practices and Recommendations

Optimizing Hadoop for MapReduce is for Hadoop administrators, MapReduce users, or interested beginners who wish to optimize their clusters and applications. Having prior knowledge of creating MapReduce applications is not necessary, but will be an added advantage.


Optimizing Hadoop for MapReduce
Learn how to configure your Hadoop cluster to run optimal MapReduce jobs

For more information, please visit: http://www.packtpub.com/learn-to-implement-and-use-hadoop-mapreduce-framework/book

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