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Hadoop: The Definitive Guide

av Tom White

MedlemmarRecensionerPopularitetGenomsnittligt betygOmnämnanden
231286,599 (3.71)1
Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters.Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you: Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using 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 Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject."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, Hadoop Founder, Yahoo!… (mer)
  1. 10
    Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies) av Jimmy Lin (billmcn)
    billmcn: Once you've mastered the basics with the O'Reilly book, this is a good next step. Lin and Dyer's book covers advanced techniques such as in-mapper combining and secondary sorts, along with discussions of advanced applications like Page Rank and Expectation Maximization.… (mer)
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Its bible of hadoop. ( )
  madhukaraphatak | Aug 12, 2020 |
A comprehensive review of the Hadoop ecosystem, with plenty of hands-on advice. Some of the content for the various sub-projects (Sqoop, Hive etc) is somewhat repetitive and lacks depth, but these are ancillary to the book's core purpose of explaining what Hadoop is about, which it achieves. ( )
  gbsallery | Mar 3, 2013 |
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Hadoop: The Definitive Guide helps you harness the power of your data. Ideal for processing large datasets, the Apache Hadoop framework is an open source implementation of the MapReduce algorithm on which Google built its empire. This comprehensive resource demonstrates how to use Hadoop to build reliable, scalable, distributed systems: programmers will find details for analyzing large datasets, and administrators will learn how to set up and run Hadoop clusters.Complete with case studies that illustrate how Hadoop solves specific problems, this book helps you: Use the Hadoop Distributed File System (HDFS) for storing large datasets, and run distributed computations over those datasets using 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 Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems If you have lots of data -- whether it's gigabytes or petabytes -- Hadoop is the perfect solution. Hadoop: The Definitive Guide is the most thorough book available on the subject."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, Hadoop Founder, Yahoo!

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