Kafka उच्च-व्हॉल्यूम, रीयल-टाइम, किंवा स्ट्रीमिंग डेटा असलेल्या अनेक परिस्थितींमध्ये वापरला जातो — मेसेजिंग, डेटा पाइपलाइनस, इव्हेंट-चालित आर्किटेक्चर, स्ट्रीम प्रोसेसिंग, लॉग एग्रीगेशन, आणि बरेच काही. वापर प्रकरण समजून घेतल्यास हे स्पष्ट होते की Kafka कोठे बसते.
सामान्य वापर प्रकरण
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✓ 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