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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

By : Kumar, Gulati
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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

2 (4)
By: Kumar, Gulati

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 (12 chapters)
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Summary


This chapter focused on handling streaming data from sources such as Kafka, socket, and filesystem. We also covered various stateful and stateless transformation of DStream along with checkpointing of data. But chekpointing of data alone does not guarantee fault tolerance and hence we discussed other approaches to make Spark Streaming job fault tolerant. We also talked about the transform operation, which comes in handy where operations of RDD API is not available in DStreams. Spark 2.0 introduced structured streaming as a separate module, however, because of its similarity with Spark Streaming, we discussed the newly introduced APIs of structured streaming also.

In the next chapter, we will focus on introducing the concepts of machine learning and then move towards its implementation using Apache Spark MLlib libraries. We will also discuss some real-world problems using Spark MLlib.

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