In developing a streaming data pipeline, which option correctly fills the blank to maintain state for late data?

The correct choice for maintaining state for late data in a streaming data pipeline is to use the watermarks feature. The method withWatermark("event_time", "10 minutes") specifies that the system should maintain the state of the data for events that arrive late, allowing for a delay of up to 10 minutes. This effectively manages and tracks the lateness of incoming events.

In a streaming context, watermarks help end the processing of event time aggregations. They allow the system to know how far behind in event time it should still consider an event as "late" rather than treating it as a dropped event. By setting a watermark, you ensure that data arriving after the watermark threshold will not contribute to any aggregations. This is crucial for operations like windowing and allows for accurate representation of state over time, which is especially important in real-time analytics.

The other options do not correctly address the requirement of maintaining state for late data within the context of a streaming data pipeline. For instance, awaitArrival and await are not standard terms used in this context, and slidingWindow does not apply directly to the concept of handling late data through watermarks. Therefore, the use of withWatermark is not only formally recognized in streaming data frameworks but also directly