data-quality-frameworks

To implement data validation in a dbt pipeline, you write SQL assertions for dbt tests and use Great Expectations to check schema, data types, and value ranges for data integrity.

Data contracts are agreements on data structure, quality, and SLAs between producers and consumers. They enforce data quality by preventing non-compliant data from propagating through your pipeline.

Yes, you can use Great Expectations alongside dbt tests. Great Expectations handles Python-based validation for schemas and ranges, while dbt tests manage SQL assertions for data modeling.

Yes, you need Python to set up data quality frameworks with Great Expectations, and SQL for dbt tests. These dependencies are required to build validation rules and ensure pipeline reliability.

The best way to validate incoming data is to apply data quality validation using Great Expectations and dbt tests. This ensures incoming data meets defined quality standards before loading.

You should establish data contracts when building data pipelines to prevent data errors from propagating. They define structure and SLA agreements, ensuring trustworthy data for analysis.