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)

What this book covers

Chapter 1, Configuring Kafka, explains the basics for getting started with Apache Kafka. It discusses how to install, configure, and run Kafka. It also discusses how to make basic operations with Kafka brokers and topics.

Chapter 2, Message Validation, explores how to program data validation for your enterprise service bus, covering how to filter messages from an input stream.

Chapter 3, Message Enrichment, looks at message enrichment, another important task for an enterprise service bus. Message enrichment is the process of incorporating additional information into the messages of your stream.

Chapter 4, Serialization, talks about how to build serializers and deserializers for writing, reading, or converting messages in binary, raw string, JSON, or AVRO formats.

Chapter 5, Schema Registry, covers how to validate, serialize, deserialize, and keep a history of versions of messages using the Kafka Schema Registry.

Chapter 6, Kafka Streams, explains how to obtain information about a group of messages – in other words, a message stream – and how to obtain additional information, such as that to do with the aggregation and composition of messages, using Kafka Streams.

Chapter 7, KSQL, talks about how to manipulate event streams without a single line of code using SQL over Kafka Streams.

Chapter 8, Kafka Connect, talks about other fast data processing tools and how to make a data processing pipeline with them in conjunction with Apache Kafka. Tools such as Apache Spark and Apache Beam are covered in this chapter.