Free PDF Hadoop Essentials: A Quantitative ApproachBy Henry H. Liu
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Hadoop Essentials: A Quantitative ApproachBy Henry H. Liu
Free PDF Hadoop Essentials: A Quantitative ApproachBy Henry H. Liu
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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 built with 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.
This textbook works with the latest Hadoop stable release of 1.0.4. It adopts a unique approach to helping developers and CS students learn Hadoop MapReduce programming fast. Rather than filled with disjointed, piecemeal code snippets to show Hadoop MapReduce programming features one at a time, it is designed to place your total Hadoop MapReduce programming learning process in a common application context of mining customer spending patterns ensconced in large volumes of credit card transaction record data. Precise, end-to-end procedures are given to help you set up your Hadoop MapReduce development environment quickly on Eclipse with Maven on Mac OS X or Linux. Step-by-step procedures are also given on how to set up a four-node Linux cluster at minimum so that you can run your MapReduce programs not only in local mode but also in fully distributed mode on a real cluster. In fact, all MapReduce programs presented in the book have been tested and verified in local mode and on such a Linux cluster.
This textbook mainly focuses on teaching Hadoop MapReduce programming in a scientific, objective, quantitative approach. Rather than heavily relying on subjective, verbose (and sometimes even pompous) textual descriptions with sparse code snippets, this textbook uses Hadoop Java APIs, Hadoop configuration parameters, complete MapReduce programs and their execution logs and outputs to demonstrate how Hadoop MapReduce framework works and how to write MapReduce programs. Specifically, this text covers the following subjects:
* Introduction to Hadoop
* Setting up a Linux Hadoop Cluster
* The Hadoop Distributed FileSystem
* MapReduce Job Orchestration and Workflows
* Basic MapReduce Programming
* Advanced MapReduce Programming
* Hadoop Streaming
* Hadoop Administration
No matter what role you play on your team, this text can help you gain truly applicable Hadoop skills in a most effective and efficient manner. The book can also be used as a supplementary textbook for a distributed computing or Hadoop course offered to upper-division college CS students.
- 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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