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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

MapReduce patterns

A MapReduce pattern is a template for solving a common and general data manipulation problem with MapReduce. A pattern is not specific to a domain, such as text processing or graph analysis, but it is a general approach to solving a problem. Using design patterns is all about using tried and true design principles to build better software. 

Design patterns have been making developers, lives easier for years. They are tools for solving problems in a reusable and general way, so that the developer can spend less time figuring out how they're going to overcome a hurdle and move on to the next one.

Aggregation patterns

This chapter focuses on design patterns that produce a top-level, summarized view...

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