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Hadoop: The Definitive Guide
 
 

Hadoop: The Definitive Guide [Kindle Edition]

Tom White
3.7 out of 5 stars  See all reviews (3 customer reviews)

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Product Description

Product Description

Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any size, and administrators will learn how to set up and run Hadoop clusters.

This revised edition covers recent changes to Hadoop, including new features such as Hive, Sqoop, and Avro. It also provides illuminating case studies that illustrate how Hadoop is used to solve specific problems. Looking to get the most out of your data? This is your book.

  • Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduce
  • Become familiar with Hadoop’s data and I/O building blocks for compression, data integrity, serialization, and persistence
  • Discover common pitfalls and advanced features for writing real-world MapReduce programs
  • Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud
  • Use Pig, a high-level query language for large-scale data processing
  • Analyze datasets with Hive, Hadoop’s data warehousing system
  • Take advantage of HBase, Hadoop’s database for structured and semi-structured data
  • Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems

"Now you have the opportunity to learn about Hadoop from a master -- not only of the technology, but also of common sense and plain talk."

--Doug Cutting, Cloudera

About the Author

Tom White has been an Apache Hadoop committer since February 2007, and is a member of the Apache Software Foundation. He works for Cloudera, a company set up to offer Hadoop support and training. Previously he was as an independent Hadoop consultant, working with companies to set up, use, and extend Hadoop. He has written numerous articles for O'Reilly, java.net and IBM's developerWorks, and has spoken at several conferences, including at ApacheCon 2008 on Hadoop. Tom has a Bachelor's degree in Mathematics from the University of Cambridge and a Master's in Philosophy of Science from the University of Leeds, UK.


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1 of 1 people found the following review helpful
Hadoop review 12 Feb 2012
By rlaenen
Format:Paperback
The book gives a decent overview of the Hadoop software.
Unfortunately, all example code is based on an old version of the Hadoop API (even in this second edition).
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1 of 1 people found the following review helpful
Format:Paperback
This book is well organized and spans the Hadoop stack. As a relative newcomer to Hadoop, I'd already read many of the online docs and surveyed some of the source code, but having this book clarified some issues and questions that had arisen. It's a useful reference if you are working with Hadoop or interested in the Hadoop stack.
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Format:Paperback
If like me, you are a developer, and want a book that focuses largely on the coding aspect of hadoop then this is the book for you e.g MapReduce, interfacing with hdfs, API's (old and new).

I found it to be an easy simple read with many examples. I was able to get through half the book in a day whilst doing some practical work alongside it.
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Popular Highlights

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&quote;
MapReduce works well on unstructured or semi-structured data, since it is designed to interpret the data at processing time. &quote;
Highlighted by 19 Kindle users
&quote;
MapReduce is a good fit for problems that need to analyze the whole dataset, in a batch fashion, particularly for ad hoc analysis. An RDBMS is good for point queries or updates, where the dataset has been indexed to deliver low-latency retrieval and update times of a relatively small amount of data. MapReduce suits applications where the data is written once, and read many times, whereas a relational database is good for datasets that are continually updated. &quote;
Highlighted by 14 Kindle users
&quote;
MapReduce is a batch query processor, and the ability to run an ad hoc query against your whole dataset and get the results in a reasonable time is transformative. &quote;
Highlighted by 12 Kindle users

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