Kafka digunakan dalam pelbagai senario yang melibatkan data bervolum tinggi, masa nyata, atau streaming — messaging, saluran data, seni bina dipacu event, stream processing, pengagregatan log, dan banyak lagi. Memahami use case menjelaskan di mana Kafka sesuai.
Use case lazim
✓ 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
