volume rules

Asserts that a table contains at least one row. This is the most fundamental volume check to confirm that a table has not been accidentally truncated, dropped, or failed to load any data.

Asserts that daily row counts fall within an expected range. Identifies days with abnormally low or high data volumes that may indicate partial loads, duplicate ingestion, or upstream source issues.

Asserts that a table has the expected number of columns. Detects unintended schema changes such as dropped columns, added columns from upstream migrations, or schema drift between environments.

Asserts that the row count of a table falls within an expected minimum and maximum range. Catches both data loss (too few rows) and data duplication or explosion (too many rows).

Asserts that a table contains at least the specified minimum number of rows. Useful for tables with known baseline volumes where dropping below a threshold indicates a data load issue.

Asserts that the current row count is within the specified number of standard deviations from the historical average. Uses statistical anomaly detection to catch unexpected volume spikes or drops without requiring hard-coded thresholds.

Asserts that the current row count has not decreased more than the specified percentage compared to the previous run's row count. Detects accidental data loss, failed incremental loads, or unintended deletions between pipeline runs.