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


In this chapter, we learnt about real-time analytics and saw how big data can be used in real-time analytics apart from batch processing too. We introduced the product Impala that can be used to fire fast SQL queries on big data which is usually stored in Parquet format in HDFS. While looking at Impala we briefly did a simple case study on flight analytics using Impala. We later covered Apache Kafka a messaging product that can be used in conjunction with big data technologies and build real time data stacks. Kafka is a scalable messaging solution and we showed how it can be integrated with Spark Streaming module of Apache Spark. Spark Streaming let's you collect data in mini batches in real time and it calls sequence of these mini batches as streams. Spark Streaming is becoming very popular these days as it is a good scalable solution that fits into the needs of many users. We finally covered a few cases studies using Apache Kafka and Spark Streaming and showed how complex use cases...