data-quality-frameworks

You can validate data quality across pipelines by building expectation suites and checkpoints in Great Expectations alongside dbt tests. This combination enforces completeness, accuracy, and freshness checks directly within your data pipelines.

Data contracts define schema, lineage, and governance rules to enforce data quality between teams. They serve as explicit agreements that ensure data conforming to business rules is delivered across production environments.

You integrate data validation checks into CI/CD pipelines by automating expectation suites, checkpoints, and dbt tests during data releases. This automates quality checks to catch accuracy and completeness issues early.

Yes, you can use dbt tests to check data freshness and uniqueness in production environments. This Skill provides patterns to implement these specific data quality checks within your dbt models and dashboards.

The best way to set up cross-team governance for data quality is by combining data contracts with automated validation checks. This approach establishes governance controls, observability, and explicit schema rules between teams.