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)

Integrated framework advantages

Kafka Stream is tightly integrated with Apache Kafka. It provides reach sets of API and offers powerful features to build the Stream processing application. If you are using Kafka as a centralized storage layer for your data and want to do Stream processing over the it, then using Kafka Stream should be preferred because of the following reasons:

  • Deployment: An application built using Kafka Stream does not require any extra setup of the clusters to run. It can be run from a single-node machine or from your laptop. This is a huge advantage over other processing tools, such as Spark, Storm, and so on, which require clusters to be ready before you can run the application. Kafka Stream uses Kafka's producer and consumer library.

If you want to increase parallelism, you just need to add more instances of the application, and Kafka Stream will...