expectations

You can enforce data quality checks by defining expectations in YAML files like table.sl.yml or task.sl.yml. These definitions validate schemas, row counts, and value distributions during loading and transformation, triggering warnings or errors when checks fail.

Built-in macros for schema and row count validation include is_col_value_not_unique, is_row_count_to_be_between, and count_by_value. These built-in functions allow you to enforce primary-key uniqueness and verify data distribution constraints directly in your pipelines.

Yes, you can extend data validation with custom macros stored in the metadata/expectations directory. This allows teams to tailor quality checks to specific domain rules beyond the standard built-in macro library.

Apply data quality expectations during loading using table.sl.yml to validate raw inputs, and during transformation using task.sl.yml to validate processed outputs. Both stages enforce schema correctness and value distributions to ensure pipeline reliability.

No, defining data quality expectations does not require external dependencies. The validation framework operates natively using YAML-based definitions and macros to enforce checks without integrating third-party data quality tools.

To trigger an error for primary-key uniqueness, define an expectation in table.sl.yml using the is_col_value_not_unique macro. This built-in check validates uniqueness during data loading and automatically raises an error if duplicates are detected.