Microsoft Fabric
Microsoft Fabric8 min read•
Data Quality in Microsoft Fabric
Ensure data reliability with quality rules and monitoring.
By Administrator
Data quality in Fabric ensures your analytics are built on reliable, accurate, and timely data through validation rules and monitoring.
Data Quality Dimensions
Accuracy Data correctly represents real-world values.
Completeness Required fields are populated; no unexpected nulls.
Consistency Same data appears the same across systems.
Timeliness Data is current and refreshed as expected.
Uniqueness No unexpected duplicates exist.
Implementing Quality Checks
In Notebooks Write quality validation code: - Check for nulls in required columns - Validate data ranges - Verify referential integrity - Detect duplicates
In Dataflows Add transformation steps: - Remove errors - Validate data types - Apply business rules
Data Activator Set up alerts: - Monitor data freshness - Detect anomalies - Alert on threshold breaches
Quality Monitoring
Build Quality Dashboards Track quality metrics over time: - Completeness percentages - Error counts - Freshness indicators
Automate Validation Run quality checks with each refresh: - Fail pipelines on critical issues - Log warnings for review - Track trends
Best Practices
- Define quality rules with business users
- Automate quality checks
- Fix issues at the source when possible
- Document known data limitations
- Regular quality reviews
Frequently Asked Questions
Does Fabric have built-in data quality tools?
Fabric provides data quality capabilities through notebooks, dataflows, and Data Activator for monitoring. Additional governance features are available through Purview integration for enterprise data catalog and quality tracking.
How do I monitor data freshness in Fabric?
Use Data Activator to create triggers on data timestamps, set up monitoring dashboards tracking last refresh times, and configure alerts when data exceeds expected staleness thresholds.
Microsoft FabricData QualityGovernanceMonitoring