data-quality-frameworks

To validate data quality, you can configure Great Expectations validations, build dbt test suites, and enforce data contracts on critical datasets. This ensures reliable pipelines by applying automated checks across environments.

Data contracts are enforced agreements that validate data quality between teams. By defining expectations and dbt tests, they ensure datasets meet strict validation criteria before moving through data pipelines.

Yes, you can automate data quality validation in CI/CD. By integrating Great Expectations validations and dbt tests, the framework monitors quality metrics and automates checks across pipeline environments.

Yes, the framework supports monitoring data quality metrics across environments. It applies Great Expectations validations, dbt tests, and data contracts with automated alerting to ensure continuous quality.

The best way to implement data contracts is by configuring Great Expectations expectations and building dbt test suites on critical datasets. This validates data quality and enforces contracts between teams.