Self-Describing Events Using Spring Kafka and Confluent Schema Registry

This post introduces a generic processor for handling diverse Protobuf event schemas using Spring Kafka and Confluent Schema Registry. It details how to use `DynamicMessage` to parse any Protobuf record at runtime, converting it to JSON for reporting systems. The post also presents a mechanism for enhancing timestamp representations within the JSON output (e.g., Unix epoch, preserved proto format) by traversing the `DynamicMessage` object as a tree structure. This enables a flexible and adaptable event processing pipeline that can handle new event schemas without code redeployments.