Quality Observability
Monitor and ensure data quality across your entire data ecosystem.
What's Included
- Observers: Core concept, lifecycle, and logs
- Observer Types: Static vs ML observers
- Observer Categories: Tests by Integrity, Uniqueness, Freshness, Volume, Drift, Schema, Custom
- Drift: Detect distribution and cardinality changes over time
- Rule Catalog: All built-in rules and templates
Coming Soon
- Scheduling - Configure when and how often quality checks run
- Federated Checks - Cross-source quality validation
- Anomaly Detection - ML-powered quality monitoring
- Custom Dimensions - Define your own quality metrics
- Quality Dashboards - Visualize quality trends
- SLA Monitoring - Track quality against service levels
Quality Dimensions
Qupid monitors seven key dimensions:
- Completeness - Are all required fields present?
- Accuracy - Does data match real-world values?
- Consistency - Is data coherent across systems?
- Timeliness - Is data fresh and up-to-date?
- Validity - Does data conform to expected formats?
- Uniqueness - Are there unwanted duplicates?
- Drift - Are data distributions changing over time?
Getting Started
- Read the Observers overview
- Pick a Category and test
- Choose a Type and set thresholds
- Schedule and monitor runs
Start with simple rules and expand coverage as you learn what matters most for your data!