stream-processing

To process unbounded event streams with exactly-once semantics, you need a system that manages bounded state and utilizes idempotent sinks to guarantee deterministic results without data duplication or loss.

Stateful processing in real-time analytics involves maintaining application state, such as user sessions or deduplication buffers, to compute continuous aggregates over unbounded event streams efficiently.

Handling event time windowing for real-time aggregation involves grouping unbounded event streams into logical time windows, allowing the system to process continuous aggregates with low latency despite out-of-order data arrival.

Yes, stream processing is designed to analyze user activity logs in real-time, tracking user sessions and detecting anomalies by applying stateful processing and continuous aggregation to the event stream.

Idempotent sinks are required for exactly-once processing to ensure that replaying events during failure recovery does not duplicate output, thereby maintaining end-to-end data consistency across sources and sinks.

The best way to track user sessions in an event stream is through stateful processing, which manages bounded state to deduplicate events and compute continuous aggregates with low latency for real-time analytics.