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

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

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

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

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

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

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

Kafka's parallel processing depends on partitions, not just consumers or threads.

Backpressure helps manage message flow in Kafka during traffic spikes.