Dataform rowConditions for Email and Date Validation

What Dataform rowConditions Assertions Are

In Google Cloud Dataform, assertions are automated data quality testing mechanisms executed during workflow runs. Within a table definition's config block, rowConditions allows data practitioners to define custom SQL expressions that every row in the resulting table must satisfy. If any row evaluates to false against any defined row condition, Dataform flags the assertion as failed.

How it Meets the Business Requirements

• Pattern Matching (✓): A custom SQL expression such as 'email like "%@%.%"' can be added directly to the rowConditions array to verify that string values conform to expected email patterns.
• Range Validation (✓): Expressions like 'signup_date is null or signup_date > "2022-08-01"' ensure that date values stay within permissible historical boundaries.
• Automated Workflow Quality Gate (✓): Dataform evaluates these row conditions every time it updates the workflow and records failing rows in an automatically created assertion view in BigQuery (under the dataform_assertions dataset).

Key Capabilities and Advantages

• Declarative Quality Control: Quality tests are declared directly inside the table's SQLX configuration block alongside transformation logic.
• Automatic Failure Detection: Any record that violates the boolean logic causes the assertion to fail, preventing silent data corruption.
• Inspection Views: Dataform automatically publishes compiled assertion queries as BigQuery views so practitioners can inspect invalid rows.
• Comprehensive Rule Support: Supports any valid SQL boolean predicate, including range checks, regex comparisons, arithmetic boundaries, and conditional logic.

Why This Is Optimal

Using rowConditions inside the table's config block provides a built-in, native method to enforce pattern and range constraints without needing to write external validation pipelines or separate testing scripts.