verify

Verify data quality across a dbt pipeline by comparing live mart values against predefined checks in a verifications seed. This classifies results by status to detect anomalies and highlight regressions after new data ingestion.

Automate cell-level data validation by running checks against a configured verifications.csv file. This surfaces data quality issues across post-ingest and post-dive stages, providing guidance for targeted remediation and gap analysis.

Run a verification cycle by querying actual mart data for a specific model, such as naic_national_kpis_by_company, and comparing the live values to predefined verifications to classify the results by status.

Yes, you need access to a verifications seed to check data quality across marts models. The Skill requires this seed and the ability to query actual mart data to classify verification results and identify anomalies.

You can use this for ongoing data governance across any marts model with configured verifications. It is applicable for both post-ingest and post-dive checks, supporting tiered verification to root-cause data quality issues.

Data quality verification may fail to surface anomalies if the verifications seed is not configured correctly or if it cannot query the actual mart data. It requires both the seed definitions and live mart access to classify results by status.