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


For our case study on customer segmentation using clustering, we will be using a dataset from UCI repository of datasets for a UK online retail store. This retail store has shared its data with UCI and the dataset is freely available on their website. This data is essentially the transactions of different customers made on the online retail store. The transactions were made from different countries and the dataset size is good (thousands of rows). Let's go through the attributes of the dataset:

Attribute name


Invoice number

Invoice number; a number uniquely assigned to each transaction

Stock code

Product (item) code; a 5-digit integral number uniquely assigned to each distinct product


Product item name


Quantity of items purchased in a single transaction

Invoice date

Date of the transaction

Unit price

Price of the item (in pounds)

Customer ID

Unique ID of the person making the transaction


Country from where...