June 2016
Intermediate to advanced
286 pages
8h 9m
English
Content preview from Data Analytics with Hadoop
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Part I. Introduction to Distributed Computing
The first part of Data Analytics with Hadoop introduces distributed computing for big data using Hadoop. Chapter 1 motivates the need for distributed computing in order to build data products and discusses the primary workflow and opportunity for using Hadoop for data science. Chapter 2 then dives into the technical details of the requirements for distributed storage and computation and explains how Hadoop is an operating system for big data. Chapters 3 and 4 introduce distributed programming using the MapReduce and Spark frameworks, respectively. Finally, Chapter 5 explores typical computations and patterns in both MapReduce and Spark from the perspective of a data scientist doing analytics on large datasets.
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