Automate Data Vault integrity testing with ad-hoc queries or Snowflake DMFs.
npx skills add https://github.com/PatrickCuba/the_data_must_flow --skill dv-test
Please help me install this Agent Skill. Skill: dv-test Source: https://github.com/PatrickCuba/the_data_must_flow/tree/main/dvos-skills/skills/dv-test Command: npx skills add https://github.com/PatrickCuba/the_data_must_flow --skill dv-test
Data Vault integrity testing is complex and time-consuming; this skill automates the process with ad-hoc validation queries or Snowflake DMF-based continuous monitoring to ensure ongoing data quality.
Attach the DMFs to vault tables and start using the ad-hoc and DMF-based tests to validate data vault integrity.
High-intent search queries and answers about installing and using this skill.
You can automate Data Vault integrity testing by attaching predefined Snowflake Data Metric Functions (DMFs) to hub, link, and satellite artefacts, enabling continuous monitoring and ad-hoc validation queries across staging and production environments.
Continuous Data Vault data quality monitoring is achieved by attaching Snowflake DMFs to vault tables, which provides near-real-time alerts after each DML commit and generates a centralized, reportable status view for ongoing oversight.
Yes, Snowflake DMFs are required for DMF-based continuous monitoring, alongside a dedicated testing schema and a defined set of DMFs to attach to your vault artefacts, though ad-hoc validation queries can also be run independently.
Yes, you can apply proactive, reactive, and periodic data quality checks to Data Vault satellites, hubs, and links using either ad-hoc validation queries or DMF-based monitoring across staging and production environments.
DMF-based monitoring supports Data Vault hub, link, and satellite artefacts, allowing you to attach 17 predefined DMFs to these tables to validate integrity and close data gaps in both staging and production environments.
Data Vault integrity test failures after DML commits occur when attached Snowflake DMFs detect data quality issues, triggering near-real-time alerts that are visible in a centralized status view to help you identify and close data gaps.
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