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 2. First Steps in Data Analysis

Let's take the first steps towards data analysis now. Spark has a very useful module, Spark. Apache Spark has a prebuilt module called as Spark SQL and this module is used for structured data processing. Using this module, we can execute SQL queries on our underlying data. Spark lets you read data from various datasources whether text, CSV, or Parquet files on HDFS or also from hive tables or HBase tables. For simple data analysis tasks, whether you are exploring your datasets initially or trying to analyze and cut a report for your end users with simple stats this module is tremendously useful.

In this chapter, we will work on two datasets. The first dataset that we will analyze is a simple dataset and the next one is a more complex real-world dataset from an e-commerce store.

In this chapter, we will cover the following topics:

  • Basic statistical analytic approaches using Spark SQL

  • Building association rules using the Apriori algorithm

  • Advantages and disadvantages...