What is windowing in Dataflow?
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Windowing in Google Cloud Dataflow (based on Apache Beam)
is a technique used to divide unbounded (streaming) data into manageable chunks, called windows, based on time or other criteria. Since data streams are continuous and potentially infinite, windowing allows you to group events for processing in batches over time intervals.
There are several types of windows:
Fixed Windows: Divide data into equal, non-overlapping time intervals (e.g., every 5 minutes)
Sliding Windows: Overlapping windows that move forward in time (e.g., a 10-minute window sliding every 5 minutes).
Session Windows: Based on periods of activity separated by a gap of inactivity (useful for user sessions).
Global Window: Includes all data without breaking into chunks (used in batch processing).
Windowing is essential for aggregations, analytics, and real-time monitoring in streaming pipelines. It works with watermarks and triggers to determine when to emit results. Without windowing, it would be impossible to compute results from a never-ending data stream.
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