Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers
Apache Spark 2.x for Java Developers by Sourav Gulati
English | 2017 | ISBN: 1787126497 | 350 Pages | EPUB, AZW3, PDF (conv) | 16 MB

Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java
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.
What You Will Learn

  • Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library.
  • Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library
  • Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library
  • Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems
  • Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark