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


Before we get our hands wet in the world of complex analytics, we will take small baby steps and learn some basic statistical analysis first. This would help us get familiar with the approach that we will be using on big data for other solutions as well. For our analysis initially we will take a simple cars JSON dataset that has details about a few cars from different countries. We will analyze it using Spark SQL and see how easy it is to query and analyze datasets using Spark SQL. Spark SQL is handy to use for basic analytics purposes and is nicely suited on big data. It can be run on massive datasets and data can reside in HDFS.

To start with a simple case study we are using a cars dataset. This dataset can be obtained from It can be obtained from link This datasets contains data about cars in different countries. It is in JSON format. It is not a very big dataset from the perspective...