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

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

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

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

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

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

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

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

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

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

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

Kafka uses partitions to allow multiple consumers to process messages in parallel.