Book Image

Building Data Streaming Applications with Apache Kafka

By : Chanchal Singh, Manish Kumar
Book Image

Building Data Streaming Applications with Apache Kafka

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.
Table of Contents (14 chapters)

Kafka Stream architecture 

Kafka Streams internally uses the Kafka producer and consumer libraries. It is tightly coupled with Apache Kafka and allows you to leverage the capabilities of Kafka to achieve data parallelism, fault tolerance, and many other powerful features.

In this section, we will discuss how Kafka Stream works internally and what the different components involved in building Stream applications using Kafka Streams are. The following figure is an internal representation of the working of Kafka Stream:

Kafka Stream architecture

Stream instance consists of multiple tasks, where each task processes non overlapping subsets of the record. If you want to increase parallelism, you can simply add more instances, and Kafka Stream will auto distribute work among different instances.

Let's discuss a few important components seen in the previous figure:

  • Stream...