Take back control of your data with Event Driven Systems
summary
Built a new customer database using Kafka and Kotlin that stores commands, events and aggregated states rather than only current rows; commands like create customer land on a command topic, validation produces past‑tense events (for example email updated) and results are emitted to a result topic so clients get synchronous success/failure. Uses state keys to prevent races, Kafka compaction plus tombstones and anonymize messages for retention, and two projections: fast queryable customer lookup and append‑only customer history.
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chapters (16)
- 0:00Introduction
- 1:13Monolithic database problems
- 3:42Event-driven systems benefits
- 6:30Apache Kafka introduction
- 7:36Core components and schema design
- 11:04Projections and use cases
- 13:12Data across domains
- 14:36Command API and sync responses
- 15:53Demo: Customer state processor
- 20:36Demo: Testing the system
- 22:28Customer data in operations
- 23:51Demo: Fetching history
- 24:33Input validation and errors
- 25:36Race conditions prevention
- 27:23Sensitive data handling
- 29:42Q&A
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