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

Setting up the project


Before writing code, let's remember the project requirements for the stream processing application. Recall that customer sees BTC price events happen in the customer's web browser and are dispatched to Kafka via an HTTP event collector. Events are created in an environment out of the control of Doubloon. The first step is to validate that the input events have the correct structure. Remember that defective events could create bad data (most data scientists agree that a lot of time could be saved if input data were clean).

Getting ready

Putting it all together, the specification is to create a stream application which does the following:

  • Reads individual events from a Kafka topic called raw-messages
  • Validates the event, sending any invalid message to a dedicated Kafka topic called invalid-messages
  • Writes the correct events to a Kafka topic called valid-messages, and writes corrupted messages to an invalid-messages topic

All this is detailed in the following diagram, the first...