Ebook Hadoop Essentials: A Quantitative Approach By Henry H. Liu
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Hadoop Essentials: A Quantitative Approach By Henry H. Liu
Ebook Hadoop Essentials: A Quantitative Approach By Henry H. Liu
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- Sales Rank: #1863396 in Books
- Published on: 2012-10-31
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .81" w x 7.50" l, 1.36 pounds
- Binding: Paperback
- 356 pages
From the Author
Update (March 14, 2014): This book has been upgraded to Hadoop 2/YARN with a new title of Hadoop 2 Essentials: An End-to-End Approach. Step-by-step procedures are given about how to set up a 4-node YARN Linux virtual cluster using VMware Workstation 10 on a Windows laptop. You can check the new edition out by searching "Hadoop 2 henry liu" on Amazon, especially if you are interested in building a similar Hadoop 2/YARN virtual cluster on a Windows PC.
Update (May 1, 2013): It has been verified that the MapReduce samples presented in the book work with the latest Hadoop stable release of 1.0.4. The upgrade procedure has been posted on the book's website.
This book teaches Hadoop MapReduce programming. Written in a unique quantitative approach, this book helps you learn all about Hadoop MapReduce programming within a matter of several weeks if you are a Java developer or within a semester in a classroom environment for upper-division CS students. Enhanced with precise procedures and working, non-word-counting MapReduce programs, you will learn not only how to write MapReduce programs but also how they work at the Hadoop API and workflow levels. The book also includes sanitized console outputs so that you can verify whether those MapReduce programs work as expected in your Mac or Linux environment. This is a proven approach to learning Hadoop MapReduce programming most effectively and efficiently. If you are a college professor and interested in an evaluation copy (U.S. only), please visit the book's website (www dot perfmath dot com) and contact the author using your verifiable email address.
About the Author
HENRY H. LIU, PHD, is a Software Engineer specializing in developing distributed, high performing, scalable enterprise software products. He is a certified Sun Enterprise Java Architect, IBM XML Developer, and Microsoft .Net Developer. Prior to becoming a software engineer, he had a physicist career and published over 30 research papers in physics while working in the national labs of China, France, Germany, and the United States. His honors include being the author of the highly acclaimed Software Performance and Scalability: A Quantitative Approach (Wiley, 2009), an Alexander von Humboldt Research Fellow in Germany, and a CMG 2004 Best Paper Award recipient for applying queuing theory to analyzing software performance. He is particularly encouraged that over 600 universities/colleges world-wide have used his works in teaching software technologies to CS students.
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