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

Basic analysis of data with Spark SQL

Spark SQL is a spark module for structured data processing. Almost all the developers know SQL. Spark SQL provides an SQL interface to your Spark data (RDDs). Using Spark SQL you can fire SQL queries or SQL-like queries on your big data set and fetch data in objects called dataframes.

A dataframe is like a relational database table. It has columns in it and we can apply functions to these columns such as groupBy, and so on. It is very easy to learn and use.

In the next section, we will cover a few examples on how we can use the dataframe and run regular analysis tasks.

Building SparkConf and context

This is just boilerplate code and is the entry point for the usage of our Spark SQL code. Every spark program will start with this boiler plate code for initialization. In this code we build the Spark configuration and then apply the configuration parameters (like application name and master location) and also build the SparkSession object. This SparkSession...