You have 3 consumers reading from 6 Kafka partitions. One consumer crashes. The remaining 2 need to pick up its partitions. That handoff isn’t as smooth as you’d hope.

Your event log has 100 million records. Key ‘user-42’ has been updated 500 times. You only care about the latest value. But deleting old entries would break consumers who haven’t caught up yet.

Events arrive out of order. User updates overwrite each other. Here’s how partition keys, sequence numbers, and causal ordering keep things straight.

Why your event-driven system is lying to you. Solving the ‘Dual Write’ problem using the Transactional Outbox pattern.

Storing events instead of current state. How event sourcing works, rebuilding state from events, and when the complexity is worth it.

Command Query Responsibility Segregation - why you might want separate models for reading and writing data. When it helps, when it’s overkill, and implementation patterns.

How to capture and stream database changes in real-time. CDC patterns, implementation approaches, and when to use it instead of application-level events.