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Kafka uses brokers for data handling and KRaft controllers for metadata management.

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

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

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

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

Kafka consumers resume processing from the last committed offset after a crash.

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

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka maintains message order within partitions using keys for routing.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

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