Understanding Row-Level Filtering

Alation Data Quality uses Soda Core filtering capabilities, allowing you to apply sophisticated conditions to subset data before validation. This enables more precise and targeted data quality checks by focusing on specific data segments.

Follow these best practices for row-level filtering:

• Performance Considerations: Use indexed columns in filters when possible.

• Data Distribution: Ensure filtered datasets are representative of quality expectations.

• Documentation: Clearly document filter rationale for future maintenance.

• Testing: Validate filter logic returns expected row counts before implementing checks.

• Maintenance: Regularly review filters as data patterns and business rules evolve.

Common Use Cases

Scenario	Filter Example	Business Value
Active Records Only	status = 'ACTIVE'	Focus on operationally relevant data
Recent Data	created_date >= CURRENT_DATE - 30	Ensure timeliness validation
Geographic Segmentation	region IN ('US', 'EU')	Regional compliance requirements
Business Hours	EXTRACT(hour FROM timestamp) BETWEEN 9 AND 17	Operational period validation
Product Categories	category = 'ELECTRONICS'	Category-specific quality rules
Customer Tiers	customer_tier = 'PREMIUM'	Tier-based service level validation