data-quality-frameworks

Automate data quality validation by implementing Great Expectations and dbt tests to ensure pipeline reliability through automated checks for completeness, uniqueness, and accuracy. This approach establishes data contracts and monitors metrics continuously.

Use dbt tests and Great Expectations together for comprehensive data validation by combining dbt's SQL-based testing with Great Expectations expectation suites. This dual approach enforces data contracts and validates completeness, consistency, and timeliness across workflows.

Yes, you need both Great Expectations and dbt installed to set up automated data quality checks. These dependencies are required to initialize validation rules, create expectation suites, and execute testing workflows for your data pipelines.

Create a new expectation suite by initializing Great Expectations in your project, then defining validation rules for data accuracy, consistency, and timeliness. The suite automates checks against your data engineering workflows to ensure reliability.

You can monitor data completeness, uniqueness, validity, accuracy, consistency, and timeliness using Great Expectations and dbt tests. These dimensions are validated through automated rules and data contracts within your pipelines.

Data quality pipelines fail during data contract validation when incoming data violates defined rules for uniqueness, completeness, or accuracy. Great Expectations and dbt tests detect these anomalies, halting execution to prevent invalid data from propagating downstream.