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

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

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka commit strategies determine how offsets are managed during message processing.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.