Kafka kuat nanging ora selamane peralatan sing tepat — luwih apik kanggo high-volume event streaming, pipeline, lan sistem event-driven, nanging nambah kerumitan operasional sing ditindakake dening peralatan sing luwih sederhana. Ngerti kapan Kafka pas (lan kapan mubazir) nglambangake pertimbangan sing sehat.
Kapan Kafka cocog
✓ HIGH-VOLUME event streaming / data → millions of events; high throughput needs
✓ DATA PIPELINES → streaming data reliably between many systems (a data backbone)
✓ MULTIPLE CONSUMERS of the same stream → many independent consumers/groups read the data
✓ EVENT-DRIVEN architecture / event sourcing → events as a durable record
✓ REPLAY needed → re-read historical events
✓ REAL-TIME stream processing / analytics
→ Kafka shines for scale, streaming, retention, and multiple consumers
