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In this article I present an example of how one can use Kafka and the Confluent ksqlDB stream processing database to process a simplified dataset of fake stock quotes. The ultimate goal of this excercise will be to user ksqlDB to inspect a stream of stock quotes for individual companies in 1 minute windows and identify when a window has introduced a new daily high or low stock price.
In this How To article I demonstrate setting up a Docker Compose based implementation of the Community Components of the Confluent Platform complete with the kafka-connect-datagen plugin for Kafka Connect to generate test and/or developement data useful for working with Kafka.
Here I present an end-to-end example of a Serverless event driven architecture using Confluent Cloud for stream processing paired with AWS Lambda for event responsive logic using the Serverless Application Model (SAM) framework. Together this architecture will compose a system for fictitious financial stock quote email alerting.
Schemas and schema registries are the governance tools that ensure vitality of data communications through configurable compatibility guardrails as patterns and rules to evolve schemas in a controlled fashion. In this article I will demonstrate how Confluent Schema Registry can be used in conjunction with JSON Schema to govern and enforce rules for schema evolution in the default backwards compatibility mode.
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