Implementing a Data Quality Monitoring with Snowflake Data Metric Functions

Get reliable data quality in Snowflake – directly in the platform, without extra tools or infrastructure. In this video, I’ll show you how to use Data Metric Functions (DMFs) to build an automated data quality monitoring setup that makes trust in your data measurable. We’ll start with the basics of DMFs, look at system DMFs vs. custom DMFs, and then walk through a real E‑commerce example: unique CUSTOMER_IDs, fresh customer data (max 10 days old), and order totals that never go negative. You’ll also see how to use Expectations to turn raw metrics into real quality rules and how to add Alerts for active monitoring. ⏳ Timestamps ⏳ 00:00 | What are Data Metric Functions (DMFs) in Snowflake? 00:58 | Setting up the demo data 03:18 | Technical Requirements for DMF 04:47 | Examination of the Data Profile 06:58 | Setting up builtin DMF 1 3:56 | Monitoring in the UI 17:46 | Monitoring in the meta tables 20:18 | Setting up custom DMF 26:45 | Creating alerts based on DMF Subscribe to this channel for more great content! 👉 http://www.snowflake.com/YTsubscribe/ Click here to start your 30-day free Snowflake trial, which includes $400 worth of free usage: 👉 https://snowflake.com/youtube-dev-trial Explore sample code, download tools, and connect with peers: 👉 https://developers.snowflake.com/ #Snowflake #AIDataCloud #AI #GenAI #ArtificialIntelligence