March 29, 2024 — Data Quality Monitoring Release Notes¶

Data Quality Monitoring and data metric functions — Preview¶

With this release, Snowflake is pleased to announce Data Quality Monitoring with data metric functions (DMFs) in preview. Data Quality Monitoring uses DMFs to continuously monitor the data quality metrics such as completeness, accuracy, uniqueness, and validity. You can use Snowflake provided system DMFs for common metrics such as row count, duplicates, and freshness. Alternatively, you can create your own custom DMFs to define metrics that are specific to your own data.

You can either use the DMF in a query to test the quality of data in your pipeline or associate the DMF to desired tables to continuously monitor its quality. The continuous monitoring can either be schedule-based for periodic measurement or trigger-based to measure only when the underlying table is modified. DMF results are recorded in a centralized event table in your Snowflake account to protect the privacy of your data. You can create dashboards, configure alerts, or query metric results directly from the event table. Furthermore, data in the event table is in the standard OpenTelemetry format for easy integration with observability tools.

For details, see Introduction to Data Quality and data metric functions.