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Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

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

Kafka uses heartbeats and timeouts to check if consumers are alive.

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

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

Kafka lag recovery time is how fast consumers can process backlogged messages.

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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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