Book Image

Apache Kafka 1.0 Cookbook

By : Alexey Zinoviev, Raúl Estrada
Book Image

Apache Kafka 1.0 Cookbook

By: Alexey Zinoviev, Raúl Estrada

Overview of this book

Apache Kafka provides a unified, high-throughput, low-latency platform to handle real-time data feeds. This book will show you how to use Kafka efficiently, and contains practical solutions to the common problems that developers and administrators usually face while working with it. This practical guide contains easy-to-follow recipes to help you set up, configure, and use Apache Kafka in the best possible manner. You will use Apache Kafka Consumers and Producers to build effective real-time streaming applications. The book covers the recently released Kafka version 1.0, the Confluent Platform and Kafka Streams. The programming aspect covered in the book will teach you how to perform important tasks such as message validation, enrichment and composition.Recipes focusing on optimizing the performance of your Kafka cluster, and integrate Kafka with a variety of third-party tools such as Apache Hadoop, Apache Spark, and Elasticsearch will help ease your day to day collaboration with Kafka greatly. Finally, we cover tasks related to monitoring and securing your Apache Kafka cluster using tools such as Ganglia and Graphite. If you're looking to become the go-to person in your organization when it comes to working with Apache Kafka, this book is the only resource you need to have.
Table of Contents (18 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Dedication
Preface

Introduction


The first two chapters were focused on how to build a Kafka cluster, run a producer, and run a consumer. Now that we have a producer of events, we will process those events.

In a nutshell, event processing takes one or more events from an event stream and applies actions to those events. In general, an enterprise service bus has commodity services, the most common services are the following:

  • Event handling
  • Data transformation
  • Data mapping
  • Protocol conversion

The operation of processing events involves the following:

  • An event stream to filter some events from the stream
  • Event validation against an event schema
  • Event enrichment with additional data
  • Event composition (aggregation) to produce a new event from two or more events 

This chapter is about message validation, the following chapters will be about enrichment and composition.

Before going into a concrete recipe, let's present a case study. Imagine that we are modeling the systems of Doubloon, a fictional company dedicated to cryptocurrency...