Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data

Refresher on graphs

In this section, we will cover some of the basic concepts of graphs; this is supposed to be a refresher section on graphs. This is a basic section; hence, if you already know this information, you can skip this section. Graphs are used in many important concepts in our day-to-day lives. Before we dive into the ways of representing a graph, let's look at some of the popular use cases of graphs (though this is not a complete list):

  • Graphs are used heavily in social networks

  • In finding driving directions via GPS

  • In many recommendation engines

  • In fraud detection in many financial companies

  • In search engines and in network traffic flows

  • In biological analysis

As you must have noted earlier, graphs are used in many applications that we might be using on a daily basis.

Graphs are a form of a data structure in computer science that help in depicting entities and the connection between them. So, if there are two entities, such as Airport A and Airport B and they are connected by a flight...