data-quality-frameworks

Automate data quality validation by integrating Great Expectations, dbt tests, and data contracts to enforce governance and generate automated quality reports across your ETL pipelines.

Data contracts codify schema, governance, and quality requirements to ensure reliable data pipelines, applying contract-enforced governance directly within your CI/CD automation processes.

Yes, you can run dbt tests and Great Expectations together to execute cross-tool validation, ensuring both transformation correctness and dataset quality across modern data warehouses.

Integrate data quality checks into CI/CD processes by automating expectation suites and data contracts to run validation automatically on data changes, producing automated quality reporting.

Initialize a Great Expectations project and create an expectation suite to validate your data, using configurable expectation suites to define and enforce quality rules across datasets.

Data quality validation is fully applicable to analytics workloads and data warehouses, supporting configurable expectation suites and cross-tool validation to maintain reliable pipeline outputs.