Instant MapReduce Patterns – Hadoop Essentials How-to

Practical recipes to write your own MapReduce solution patterns for Hadoop programs
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Instant MapReduce Patterns – Hadoop Essentials How-to

Srinath Perera

Practical recipes to write your own MapReduce solution patterns for Hadoop programs

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Book Details

ISBN 139781782167709
Paperback60 pages

Book Description

MapReduce is a technology that enables users to process large datasets and Hadoop is an implementation of MapReduce. We are beginning to see more and more data becoming available, and this hides many insights that might hold key to success or failure. However, MapReduce has the ability to analyze this data and write code to process it.

Instant MapReduce Patterns – Hadoop Essentials How-to is a concise introduction to Hadoop and programming with MapReduce. It is aimed to get you started and give you an overall feel for programming with Hadoop so that you will have a well-grounded foundation to understand and solve all of your MapReduce problems as needed.

Instant MapReduce Patterns – Hadoop Essentials How-to will start with the configuration of Hadoop before moving on to writing simple examples and discussing MapReduce programming patterns.

We will start simply by installing Hadoop and writing a word count program. After which, we will deal with the seven styles of MapReduce programs: analytics, set operations, cross correlation, search, graph, Joins, and clustering. For each case, you will learn the pattern and create a representative example program. The book also provides you with additional pointers to further enhance your Hadoop skills.

Table of Contents

Chapter 1: Instant MapReduce Patterns – Hadoop Essentials How-to
Writing a word count application using Java (Simple)
Writing a word count application with MapReduce and running it (Simple)
Installing Hadoop in a distributed setup and running a word count application (Simple)
Writing a formatter (Intermediate)
Analytics – drawing a frequency distribution with MapReduce (Intermediate)
Relational operations – join two datasets with MapReduce (Advanced)
Set operations with MapReduce (Intermediate)
Cross correlation with MapReduce (Intermediate)
Simple search with MapReduce (Intermediate)
Simple graph operations with MapReduce (Advanced)
Kmeans with MapReduce (Advanced)

What You Will Learn

  • Write and run a simple MapReduce program
  • Understand the workings of Hadoop and how to write a custom formatter
  • Calculate analytics, cross-correlation, and set operations using Hadoop
  • Write simple Hadoop programs to perform searches
  • Join data by writing Hadoop programs
  • Perform graph operations and clustering

Authors

Table of Contents

Chapter 1: Instant MapReduce Patterns – Hadoop Essentials How-to
Writing a word count application using Java (Simple)
Writing a word count application with MapReduce and running it (Simple)
Installing Hadoop in a distributed setup and running a word count application (Simple)
Writing a formatter (Intermediate)
Analytics – drawing a frequency distribution with MapReduce (Intermediate)
Relational operations – join two datasets with MapReduce (Advanced)
Set operations with MapReduce (Intermediate)
Cross correlation with MapReduce (Intermediate)
Simple search with MapReduce (Intermediate)
Simple graph operations with MapReduce (Advanced)
Kmeans with MapReduce (Advanced)

Book Details

ISBN 139781782167709
Paperback60 pages
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