{"id":542510,"date":"2026-07-27T16:30:46","date_gmt":"2026-07-27T14:30:46","guid":{"rendered":"https:\/\/silicon-saxony.de\/adesso-implementing-data-quality-monitoring-using-data-metric-functions-in-snowflake\/"},"modified":"2026-07-27T16:30:46","modified_gmt":"2026-07-27T14:30:46","slug":"adesso-implementing-data-quality-monitoring-using-data-metric-functions-in-snowflake","status":"publish","type":"post","link":"https:\/\/silicon-saxony.de\/en\/adesso-implementing-data-quality-monitoring-using-data-metric-functions-in-snowflake\/","title":{"rendered":"adesso: Implementing Data Quality Monitoring Using Data Metric Functions in Snowflake"},"content":{"rendered":"

<\/p>\n

Basics of DMFs <\/h3>\n

DMFs are functions that return a specific metric, such as the number of NULL values in a column, the number of unique values, or the freshness of a table. The system DMFs provided by Snowflake in the SNOWFLAKE.CORE schema cover many standard requirements and enable a quick start without additional development effort. For more complex or domain-specific use cases, you can create custom DMFs and store them flexibly in the desired database and schema.<\/p>\n

Once a DMF is assigned to a table or view, Snowflake handles the automatic execution. The metrics are calculated at defined intervals\u2014for example, several times a day or aligned with the respective data load cycle. All results are stored centrally in a dedicated event table, creating a consistent and traceable history of data quality. This approach not only makes data quality visible but also allows for comparison over time and across different data objects.<\/p>\n

DMFs can be applied to a wide variety of objects, including tables, views, materialized views, dynamic tables, external tables, and Apache Iceberg tables. As a result, the approach is suitable for both traditional analytics workloads and modern data architectures. At the same time, there are intentionally set limits\u2014such as on the maximum number of DMF mappings or on usage in shared or trial accounts\u2014that should be taken into account during planning.<\/p>\n

Added Value and Potential Applications of DMFs <\/h3>\n

DMFs enable the continuous measurement of quality metrics such as completeness, uniqueness, and timeliness, rather than checking them only sporadically or manually. This allows for the automation of error-correction measures and ensures high-quality data on an ongoing basis, which in turn strengthens all stakeholders\u2019 trust in this data. To this end, Snowflake provides both predefined, system-native DMFs and the ability to define custom metrics that are precisely tailored to business or technical requirements.<\/p>\n

Data Quality and DMFs are available as a feature of Snowflake Enterprise Edition. One advantage of the Snowflake approach lies in its technical implementation. DMFs utilize serverless compute resources that are fully managed by Snowflake. There is no need to provision or manage additional virtual warehouses, and billing is based solely on the scheduled execution of the metrics. Creating DMFs is free of charge, and ad-hoc calls\u2014such as those made for analysis\u2014also incur no costs. Usage is transparently reported under \u201cData Quality Monitoring\u201d and can be tracked in detail.<\/p>\n

Practical Application of DMFs <\/h3>\n

An example implementation scenario in an e-commerce business illustrates what such a data quality workflow looks like in practice. <\/p>\n

Specifically, the example implements the following quality rules, among others:<\/p>\n