Kafka se koristi u mnogim scenarijima koji uključuju visok volumen, podatke u realnom vremenu ili streaming podatke — razmjenu poruka, podatkovne cjevovode, event-driven arhitekture, stream processing, agregaciju dnevnika i još mnogo toga. Razumijevanje slučajeva korištenja pojašnjava gdje se Kafka uklapa.
Česti slučajevi korištenja
✓ MESSAGING / event streaming → decoupled pub/sub between services at scale
✓ DATA PIPELINES / integration → reliably stream data between systems (databases, services,
data warehouses, analytics) — a central data "backbone"
✓ EVENT-DRIVEN ARCHITECTURE → services emit and react to events; event sourcing (events as
the source of truth)
✓ STREAM PROCESSING → real-time processing/analytics on event streams (Kafka Streams, Flink)
✓ LOG AGGREGATION → collect logs/metrics from many services into one stream
✓ ACTIVITY TRACKING → user activity, clickstreams, telemetry at high volume
✓ CHANGE DATA CAPTURE (CDC) → stream database changes to other systems
✓ METRICS / monitoring → real-time metrics collection and processing
