A global window runs the validation against the entire dataset on every execution. There is no time-based partitioning — every row in the table is included each time.Best for: Small tables, or when you need a full-dataset view every run.Trade-offs: On large tables, global windows perform a full data load on every polling cycle, which can cause performance degradation and high resource consumption.No additional configuration is required for global windows.
A tumbling window divides data into fixed, non-overlapping time segments based on a timestamp column. Each segment is validated independently, making it easier to identify issues in a specific batch or time period.Best for: Large tables, continuous monitoring of recent data, identifying temporal anomalies, and batch-specific quality checks.
Given a Look Back Period of 10 days and a Window Size of 2 days, Datachecks creates 5 non-overlapping windows. Each window is validated independently. Only the data within the Look Back Period is considered — older records are excluded.Example:
Look Back Period: 10 daysWindow Size: 2 days→ Window 1: days 1–2→ Window 2: days 3–4→ Window 3: days 5–6→ Window 4: days 7–8→ Window 5: days 9–10
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