Documentation

Data Quality Frameworks

Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.

●Implementing data quality checks in pipelines

●Setting up Great Expectations validation

●Building comprehensive dbt test suites

●Establishing data contracts between teams

●Monitoring data quality metrics

●Automating data validation in CI/CD

●The data sources are undefined or unavailable

●You cannot modify validation rules or schemas

●The task is unrelated to data quality or contracts

●Identify critical datasets and quality dimensions.

●Define expectations/tests and contract rules.

●Automate validation in CI/CD and schedule checks.

●Set alerting, ownership, and remediation steps.

●If detailed patterns are required, open resources/implementation-playbook.md.

●Avoid blocking critical pipelines without a fallback plan.

●Handle sensitive data securely in validation outputs.

●resources/implementation-playbook.md for detailed frameworks, templates, and examples.

data-quality-frameworks