Data Engineering → Data Quality & Governance
Data Quality & Governance
We implement the data quality checks, cataloguing, and access governance that make a data platform something the business can actually trust — not just query. On Databricks estates this centres on Unity Catalog for cataloguing, lineage, and access policy; alongside it, our Karsient Veriq product continuously monitors data quality and flags issues before they reach downstream reports, rather than being discovered by an analyst after the fact.
What's included
Core capabilities
Automated data quality rules & monitoring with Karsient Veriq
Unity Catalog / catalog & lineage implementation
Access policy design & role-based governance
Data contracts between producing & consuming teams
Use case
Catching data quality issues before they reach a dashboard
Scenario
A governed Databricks estate with Unity Catalog access policies in place, but no automated way to catch schema drift or bad records before they landed in gold-layer tables feeding executive dashboards.
Approach
We layered Karsient Veriq on top of the existing Unity Catalog setup — defining quality rules per table, flagging anomalies at ingestion, and routing failures for review before promotion to the gold layer.
Outcome
Data quality issues are now caught and quarantined before they reach a report, instead of being found after the fact.
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