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

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

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

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

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

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

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

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

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

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

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

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

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.