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Pro Apache Hadoop

Autor Jason Venner, Sameer Wadkar, Madhu Siddalingaiah
en Limba Engleză Paperback – 9 sep 2014
Pro Apache Hadoop, Second Edition brings you up to speed on Hadoop – the framework of big data. Revised to cover Hadoop 2.0, the book covers the very latest developments such as YARN (aka MapReduce 2.0), new HDFS high-availability features, and increased scalability in the form of HDFS Federations. All the old content has been revised too, giving the latest on the ins and outs of MapReduce, cluster design, the Hadoop Distributed File System, and more. This book covers everything you need to build your first Hadoop cluster and begin analyzing and deriving value from your business and scientific data. Learn to solve big-data problems the MapReduce way, by breaking a big problem into chunks and creating small-scale solutions that can be flung across thousands upon thousands of nodes to analyze large data volumes in a short amount of wall-clock time. Learn how to let Hadoop take care of distributing and parallelizing your software—you just focus on the code; Hadoop takes care of the rest.
  • Covers all that is new in Hadoop 2.0
  • Written by a professional involved in Hadoop since day one
  • Takes you quickly to the seasoned pro level on the hottest cloud-computing framework  
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Specificații

ISBN-13: 9781430248637
ISBN-10: 1430248637
Pagini: 500
Ilustrații: XXII, 444 p. 70 illus.
Dimensiuni: 178 x 254 x 25 mm
Greutate: 0.76 kg
Ediția:2nd ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

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Notă biografică

Jason Venner has more than 20 years of software engineering, managing, designing, and coding experience. He has been a vice president, director, and consultant. Currently, his interests and expertise are in Java, Hadoop, cloud computing, and more. For more, visit www.prohadoopbook.com.

Caracteristici

Pro Apache Hadoop, Second Edition brings you up to speed on Hadoop – the framework of big data – helping you build resilient and reliant compute clusters capable of analyzing large volumes of data in amazingly short times.