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Java Data Analysis

Java Data Analysis

By : John R. Hubbard
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Java Data Analysis

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (14 chapters)
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13
Index

Hadoop MapReduce

After installing Hadoop you can run its version of MapReduce quite easily. As we have seen, this amounts to writing your own versions of the map() and reduce() methods to solve the particular problem. This is done by extending the Mapper and Reducer classes defined in the package org.apache.hadoop.mapreduce.

For example, to implement the WordCount program, you could set your program up like the one shown in Listing 11-5.

Hadoop MapReduce

Listing 11-5. WordCount program in Hadoop

The main class has two nested classes named WordCountMapper and WordCountReducer. These extend the corresponding Hadoop Mapper and Reducer classes, with a few details omitted. The point is that the map() and reduce() methods, that are to be written, are defined in these corresponding classes. This structure is what makes the Hadoop MapReduce framework an actual software framework.

Note that the Text class used in the parameter lists at lines 11 and 17 are defined in the org.apache.hadoop.io package.

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