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)

Enriching the messages

Now, we will recap the steps of our processing engine for Monedero. The customer consults the ETH price in the client's browser and is sent to Kafka through some HTTP event collector.

The first step in our flow is the event correctness validation; remember from the previous chapter that the messages with defects are derived from bad data and that is why they are filtered. The second step now is to enrich our message with geographic location information.

Here are the architecture steps for the Monedero processing engine:

  1. Read the individual events from a Kafka topic called input-messages
  2. Validate the message, sending any defective event to a dedicated Kafka topic called invalid-messages
  3. Enrich the message with the geographic location data
  4. Write the enriched messages in a Kafka topic called valid-messages

All of these steps of the second version of...