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)

Message processing semantics

Exactly-once delivery is the holy grail of streaming analytics. Having duplicates of events processed in a streaming job is inconvenient and often undesirable, depending on the nature of the application. For example, if billing applications miss an event or process an event twice, they could lose revenue or overcharge customers. Guaranteeing that such scenarios never happen is difficult; any project seeking such a property will need to make some choices with respect to availability and consistency. One main difficulty stems from the fact that a streaming pipeline might have multiple stages, and exactly-once delivery needs to happen at each stage. Another difficulty is that intermediate computations could potentially affect the final computation. Once results are exposed, retracting them causes problems.

It is useful to provide exactly-once guarantees...