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Kafka uses replication and in-sync replicas to ensure data is always available.

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

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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