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

Event time is when an event occurs, while processing time is when it is handled.

Batch processing collects data to process later, while stream processing handles data in real-time.

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

Late-arriving events can be dropped, updated, or routed based on system needs.

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

Watermarks help stream processing systems manage event time and late events.

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.

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