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

Chapter 11. Massive Graphs on Big Data

Graph theory is one of the most important and interesting concepts in computer science. Graphs have been implemented in real life in a lot of use cases. If you use a GPS on your phone or a GPS device and it shows you driving directions to a place, behind the scenes, there is an efficient graph that is working for you to give you the best possible directions. In a social network, you are connected to your friends and your friends are connected to other friends, and so on. This is a massive graph running in production in all the social networks that you use. You can send messages to your friends, follow them, or get followed, all in this graph. Social networks or a database storing driving directions all involve massive amounts of data, and this is not data that can be stored on a single machine; instead, this is distributed across a cluster of thousands of nodes or machines. This massive data is nothing but big data and, in this chapter, we will learn...