Data Validation Skill

Data quality validation for synthetic datasets involves checking required fields, unique constraints, range and pattern validation, and referential integrity. It assigns an overall quality score and identifies specific issues against complex business logic.

Yes, data validation checks referential integrity and range constraints. The validation scopes include required fields, unique constraints, range and pattern validation, and complex business logic to ensure comprehensive data integrity verification.

You get a data quality score by running the validate_quality tool on your generated_data.csv file against provided validation rules. The tool assigns an overall quality score and identifies specific issues with your synthetic dataset.

Yes, you can use data validation to check minimum value requirements for premium customer orders. The skill validates complex business logic, ensuring premium customer orders meet minimum value requirements and transaction amounts fall within acceptable ranges.

Basic data quality checks often lack advanced validation for complex business logic and statistical properties. This skill addresses those limitations by providing comprehensive validation, quality scoring, and specific issue identification beyond basic constraint checks.

Yes, you need to define validation rules before checking data integrity. The skill validates your generated data in 'generated_data.csv' against the provided validation rules to ensure compliance with business constraints and quality metrics.