D. Tumbling
Here's why:
• Tumbling window is a fixed-size, non-overlapping window that processes the data in discrete chunks. Each event is part of exactly one window, and there are no gaps or overlaps between windows, which fits the requirement of continuous aggregation without gaps or overlaps.
Other options:
• Sliding window allows overlapping and could lead to redundant computations, which is not suitable for this requirement.
• Hopping window can produce overlapping windows, which doesn't align with the requirement to avoid overlap.
• Session window is useful for identifying periods of activity followed by inactivity, but it would not be suitable for continuous aggregation as required in this scenario.
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
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