Book Image

Apache Kafka Quick Start Guide

By : Raúl Estrada
Book Image

Apache Kafka Quick Start Guide

By: Raúl Estrada

Overview of this book

Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the ?y. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines. This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment. Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows.
Table of Contents (10 chapters)


In this chapter, we showed, instead of sending data in JSON format, how to use AVRO as the serialization format. The main benefit of AVRO (over JSON, for example) is that the data must conform to the schema. Another advantage of AVRO over JSON is that the messages are more compact when sent in binary format, although JSON is human readable.

The schemas are stored in the Schema Registry, so that all users can consult the schema version history, even when the code of the producers and consumers for those messages is no longer available.

Apache Avro also guarantees backward and forward compatibility of all messages in this format. Forward compatibility is achieved following some basic rules, such as when adding a new field, declaring its value as optional.

Apache Kafka encourages the use of Apache Avro and the Schema Registry for the storage of all data and schemas in Kafka...