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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.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

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

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

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

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

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

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

Kafka's assign() method allows manual control of partition assignments for consumers.

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

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

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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