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 12. Real-Time Analytics on Big Data

At some point in time we might all have used insurance quotes. To get insurance quotes for a car we fill in the details about us and based on our credit history and other details the application gives you the insurance quotes in real time. This application analyzes your data in real time and based on it predicts the quotes. For years, these applications have followed mostly rule-based approaches with a powerful rule engine running behind the scenes, more recently these applications have started using machine learning to analyze data further and make predictions at that point in time. All these predictions and analysis that happen at that instance or point in time are real-time analytics. Some of the most popular websites, such as Netflix or famous ad networks, are all using real-time analytics and with the coming of new devices as part of the Internet of things or IoT wave, collection and analysis of data in real time has become the need of the...