How to Build a Data Quality Framework in Databricks with Great Expectations

Stop shipping bad data! In this deep dive, we’re setting up Great Expectations (GX) on Databricks to build a production-ready data quality framework. Whether you are using Unity Catalog, Spark DataFrames, or SQL connection strings, this guide covers the most efficient ways to automate your data validation.

🚀 Check Out My Written Courses!: https://whop.com/the-data-guy-llc/

⚡ Follow my Substack: https://substack.com/@thedataguygeorge

🚀 Get Source Code and Bonus Content: https://patreon.com/TheDataGuy?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink

We walk through the entire GX lifecycle: from cluster init scripts and ephemeral contexts to building Expectation Suites that catch nulls, duplicates, and schema drift before they hit your production tables.

Видео How to Build a Data Quality Framework in Databricks with Great Expectations канала The Data and AI Guy