data-quality-frameworks

To set up Great Expectations validation for a data pipeline, configure an expectation suite to define data quality checks, then integrate it into your CI/CD systems to automatically validate data and prevent bad records from propagating downstream.

You use dbt tests to ensure data quality by defining validation rules like uniqueness and referential integrity directly in your dbt models, executing them during pipeline runs to catch anomalies and guarantee trustworthy analytics.

Data contracts are explicit agreements defining data structure and quality expectations between producers and consumers. You need them for pipeline validation to enforce strict schema rules, prevent breaking changes, and maintain reliable data pipelines.

Yes, you can integrate automated data quality checks into existing CI/CD systems by running dbt tests and Great Expectations suites during deployment, which continuously monitors data pipelines and enforces validation rules.

The best way to prevent bad data from propagating in data pipelines is implementing a testing pyramid using data contracts, Great Expectations, and dbt tests to validate dimensions like uniqueness and referential integrity before data reaches analytics.

Yes, you need a data warehouse or similar data platform because dbt tests and data contracts require integration with data warehousing tools to execute validation queries and enforce quality dimensions across your data models.