Book Image

Apache Spark 2 for Beginners

By : Rajanarayanan Thottuvaikkatumana
Book Image

Apache Spark 2 for Beginners

By: Rajanarayanan Thottuvaikkatumana

Overview of this book

<p>Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists.</p> <p>This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.</p> <p>By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark.</p>
Table of Contents (15 chapters)
Apache Spark 2 for Beginners
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Kafka stream processing


The log event processor example covered in this chapter was listening to a TCP socket for the stream of messages to be processed by the Spark Streaming data processing application. But in real-world use cases, this is not going to be the case.

Message queueing systems with publish-subscribe capability are generally used for processing messages. The traditional message queueing systems failed to perform because of the huge volume of messages to be processed per second for the needs of large-scale data processing applications.

Kafka is a publish-subscribe messaging system used by many IoT applications to process a huge number of messages. The following capabilities of Kafka made it one of the most widely used messaging systems:

  • Extremely fast: Kafka can process huge amounts of data by handling reading and writing in short intervals of time from many application clients

  • Highly scalable: Kafka is designed to scale up and scale out to form a cluster using commodity hardware...