What is watermarking in streaming pipelines?
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What is Watermarking in Streaming Pipelines?
Watermarking in streaming data pipelines is a concept used to track the progress of event time in a data stream. In real-time data processing, events may arrive late or out of order due to network delays, retries, or system latencies. To handle such scenarios effectively, stream processing engines like Apache Beam (used in GCP Dataflow) introduce the idea of watermarks.
A watermark is a threshold that indicates that all events with a timestamp earlier than the watermark are expected to have already arrived. It’s a heuristic estimate of event completeness. For instance, if the watermark is at 10:00 AM, the system assumes that most events with timestamps before 10:00 AM have already been processed.
This mechanism helps the pipeline decide when to trigger window computations, such as aggregations or joins, without waiting forever for late events. Watermarking ensures a balance between timeliness and accuracy. However, it doesn’t guarantee completeness—it’s only an approximation.
GCP Dataflow allows configuring late data handling using allowed lateness, so that even after a watermark passes, the system can still accept and process late-arriving events for a certain duration.
In summary, watermarking is crucial for managing out-of-order data in stream processing. It enables timely computation and provides flexibility to handle late data gracefully, improving both performance and reliability in real-time analytics.
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