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


A customer using an online e-commerce store to buy a phone would generally type those words in the search box at the top of the site. As soon as you type your search query, the search results are displayed at the bottom, and on the left-hand side of the page you get a list of categories that you might be interested in based on the search text you just entered. The sub-search categories are shown in the following screenshot. How did the search engine figure out these sub-search categories just based on the searched text? Well, this is what clustering is used for. It's a no-brainer that the site's search engine is advanced and must be using some form of clustering technique to group the search results so as to form useful sub-search categories:

As seen in the preceding screenshot, the left-hand side shows the categories (groups) that are generated once the user searches for a term such as car. The left-hand side looks quite relevant as we are seeing sub-categories for car accessories...