In designing a topic structure for Apache Kafka, it's essential to balance performance considerations with data integrity and modeling aspects. The common wisdom is to group events of the same type in the same topic, but this approach can lead to issues when using different topics for various event types. Instead, consider grouping events based on their logical relationships or business requirements. For example, if you're handling customer-related events, it's crucial to maintain order and ensure that related events are processed sequentially. Using a timestamp-based ordering approach is not sufficient in stream processes, as clocks can be desynchronized. Confluent Schema Registry provides flexibility in handling multiple event types in a single topic, especially with the new configuration options for constructing subject names. Ultimately, the goal is to create a topic structure that balances performance and data integrity while supporting your specific use case.