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)

Late event processing

Previously, we talked about message processing, but now we will talk about events. An event in this context is something that happens at a particular time. An event is a message that happens at a point in time.

In order to understand events, we have to know the timestamp semantics. An event always has two timestamps, shown as follows:

  • Event time: The point in time when the event happened at the data source
  • Processing time: The point in time when the event is processed in the data processor

Due to limitations imposed by the laws of physics, the processing time will always be subsequent to and necessarily different from the event time, for the following reasons:

  • There is always network latency: The time to travel from the data source to the Kafka broker cannot be zero.
  • The client could have a cache: If the client cached some events before, send them to...