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

Real-time analytics

As is evident from the name, real-time analytics provides analysis and their results in real time. Big data has mostly been used in batch mode where the queries on top of the data run for a long time and the result is later analysed. The approach is changing lately, mainly due to the new requirements pertaining to certain use cases that require immediate results. Real-time requires a separate set of architecture that caters to not only data collection and data parsing, but also data analyzing at the same time.

Let's try to understand the concept of real-time analytics using the following diagram:

As you can see, today the sources of data are plenty whether it's mobile devices, websites, third-party applications, or even the Internet of Things (sensors). All this data needs a way to propagate and flow from the source of their devices to the central unit where the data can be parsed, cleaned, and finally ingested. It is at this ingestion time that the data can also be analyzed...