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)

Best practices

Hopefully, at this juncture, you are very well aware of Kafka Producer APIs, their internal working, and common patterns of publishing messages to different Kafka topics. This section covers some of the best practices associated with Kafka producers. These best practices will help you in making some of the design decisions for the producer component.

Let's go through some of the most common best practices to design a good producer application:

  • Data validation: One of the aspects that is usually forgotten while writing a producer system is to perform basic data validation tests on data that is to be written on the Kafka cluster. Some such examples could be conformity to schema, not null values for Key fields, and so on. By not doing data validation, you are risking breaking downstream consumer applications and affecting the load balancing of brokers as data...