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

Introduction to machine learning

Spark MLlib is a general purpose machine learning library that gives all the benefits of Spark, that is, distributed computing, scalability, and fault tolerance along with easy inter-operability among different Spark modules and other libraries. Machine learning is not a new concept and certainly not solely developed by Spark, what makes Spark MLlib stand out on its own is its ease of use and generalization in developing any ML algorithm using pipeline. Again, pipeline as a concept has been used by the scikit-learn library and Apache Spark has done a brilliant job by using the same concept, but in a distributed mode. Generally, Spark's machine learning module ships:

  1. Common machine learning algorithms.
  2. Tools to load, extract, transform, and select features.

  1. The ability to chain multiple operations using pipeline.
  2. The ability to save and load algorithms, models, and pipelines.
  3. The capability of performing linear algebra and statistical operations.

Over the years...