Book Image

Apache Spark 2.x for Java Developers

By : Sourav Gulati, Sumit Kumar
Book Image

Apache Spark 2.x for Java Developers

By: Sourav Gulati, Sumit Kumar

Overview of this book

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Introducing Spark Streaming

With the advancement and expansion of big data technologies, most of the companies have shifted their focus towards data-driven decision making. It has now become an essential and integral part of the business. In the current world, not only the analytics is important, but also how early it is made available is important. Offline data analytics, as known as batch analytics, help in providing analytics on the history data. On the other hand, online data analytics showcase what is happening in real time. It helps organizations to take decisions as early as possible to keep themselves ahead of their competitors. Online analytics/near real time analytics is done by reading incoming streams of data, for example user activities for e-commerce websites, and process those streams to get valuable results.

The Spark Streaming API is a library that allows you to process data from live streams at near real time. It provides high scalability, fault tolerance, high throughput...