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

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

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

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

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 commit strategies determine how offsets are managed during message processing.

Kafka's `seek()` lets you replay only specific messages, saving time.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

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