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)

Alerting and monitoring

If you have properly configured the Kafka cluster and it is functioning well, it can handle a significant amount of data. If you have Kafka as a centralized messaging system in your data pipeline and many applications are dependent on it, any cluster disaster or bottleneck in the Kafka cluster may affect the performance of all application dependent on Kafka. Hence, it is important to have a proper alerting and monitor system in place that gives us important information about the health of the Kafka cluster.

Let's discuss some advantages of monitoring and alerting:

  • Avoid data loss: Sometimes it may happen that topic partitions are under replicated, meaning they have fewer number of replicas available in the cluster. If there are more such partitions, the risk of losing data for partition increases. A proper triggering system may help us avoid such...