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
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we discussed machine learning as a field of study and its various sub classifications. We then progressed ahead to discuss various parlance associated with machine learning and how machine learning has been implemented in Apache Spark. We also covered the datatypes that are used in the spark.mllib package and then discussed job chaining using pipeline. Terminologies around pipeline were also discussed in detail along with use cases. Finally, operations on features what a feeling discussed.

In the following chapter, we will look into another module of Spark, that is, GraphX, and we will discover types of GraphX RDD and various operations associated with them. We will also discuss use cases around GraphX implementation.