What do temporal window transformations enable you to do?

What do temporal window transformations enable you to do?

• A. Automatically enable and disable streaming at scheduled times.
• B. Aggregate event data in a stream based on specific time periods.
• C. Delete expired data after a specified retention period.
• D. Filter events by a static property value.

Answer: (B)

Temporal window transformations focus on grouping streaming data by time intervals so you can compute metrics over consistent periods. By applying a window, you divide the continuous stream into time buckets (like 5-minute or 1-hour windows). Within each bucket you can perform aggregations such as counts, sums, or averages, giving you time-based insights like total events per minute or average latency per hour. This time-bounded aggregation is what makes window transformations powerful for real-time analytics. Other options describe different capabilities that aren’t about grouping by time intervals: scheduling the streaming itself, deleting data based on retention, or filtering events by a static value. Window-based aggregation specifically enables the time-based grouping and calculation across a stream.

Temporal window transformations focus on grouping streaming data by time intervals so you can compute metrics over consistent periods. By applying a window, you divide the continuous stream into time buckets (like 5-minute or 1-hour windows). Within each bucket you can perform aggregations such as counts, sums, or averages, giving you time-based insights like total events per minute or average latency per hour. This time-bounded aggregation is what makes window transformations powerful for real-time analytics.

Other options describe different capabilities that aren’t about grouping by time intervals: scheduling the streaming itself, deleting data based on retention, or filtering events by a static value. Window-based aggregation specifically enables the time-based grouping and calculation across a stream.