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

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

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

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

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

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

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

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

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

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.

CDC with Kafka allows real-time updates from databases to multiple services.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Kafka consumer offsets help track message processing to avoid loss and duplication.

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