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)

Playing with Avro using Schema Registry

Schema Registry allows you to store Avro schemas for both producers and consumers. It also provides a RESTful interface for accessing this schema. It stores all the versions of Avro schema, and each schema version is assigned a schema ID.

When the producer sends a record to Kafka topic using Avro Serialization, it does not send an entire schema, instead, it sends the schema ID and record. The Avro serializer keeps all the versions of the schema in cache and stores data with the schemas matching the schema ID.

The consumer also uses the schema ID to read records from Kafka topic, wherein the Avro deserializer uses the schema ID to deserialize the record.

The Schema Registry also supports schema compatibility where we can modify the setting of schema compatibility to support forward and backward compatibility.

Here is an example of Avro...