data-quality-frameworks

Data validation pipelines ensure data integrity by implementing automated checks for completeness, uniqueness, and validity using Great Expectations, dbt tests, and data contracts before loading data into your warehouse.

The best way to enforce data contracts is by defining clear agreements on data structure and quality, utilizing dbt tests for data modeling validation and schema enforcement to prevent bad data from propagating.

Yes, you can use Great Expectations to define and run expectations alongside dbt tests for data modeling validation, creating a comprehensive framework that ensures data reliability and consistency across your data assets.

Automatically validate incoming customer data by configuring validation rules that check for duplicate email addresses, enforce schema requirements, and verify that critical fields like created_at are within an acceptable freshness window.

You need automated validation rules in your data pipelines when you must ensure data accuracy, consistency, and timeliness, preventing bad data from propagating through your systems and ensuring trustworthy analytics and operations.