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CDC with Kafka allows real-time updates from databases to multiple services.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

You can replay Kafka events safely by separating live and replay processes.

Kafka uses state stores to remember previous events in stateful stream processing.

Kafka can help ensure messages are processed once, but requires careful design.

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka Streams uses local state for quick data access and changelogs for recovery.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.