Data Quality
Monitor, measure, and improve data reliability with automated checks and intelligent alerting.
Trust Your Data
Bad data is expensive. When dashboards show stale numbers, reports contain duplicates, or ML models train on incomplete datasets, the cost shows up in bad decisions, lost revenue, and broken trust.
Qarion's data quality engine lets you define checks that run on schedule, monitor results over time, and alert the right people when quality drops below acceptable thresholds.
Capabilities
SQL-Based Quality Checks
Write any validation logic as a SQL check — freshness, row counts, null rates, referential integrity, custom business rules. Checks run against your warehouse on a configurable schedule.
- PostgreSQL, Snowflake, BigQuery, and Databricks support
- Configurable cron schedules
- Parameterized check templates
- Historical results tracking and trend visualization
Data Contracts & SLAs
Formalize agreements between data producers and consumers. Define what "good" looks like — freshness thresholds, completeness targets, accuracy rules — and track compliance over time.
- Define SLAs per dataset or product
- Track compliance percentage over time
- Automatic breach detection and notification
- Producer-consumer relationship mapping
Intelligent Alerting
Route alerts to the right channels and people. Integrate with Slack and Microsoft Teams for real-time notifications, aggregate related alerts to reduce noise, and use AI anomaly explanations to start investigations with context.
- Slack and Teams integration
- Severity-based routing
- Alert aggregation and deduplication
- AI root-cause hypotheses for anomaly alerts
AI Quality Suggestions
Use AI to recommend checks from schema, field types, and existing monitoring coverage. Stewards can review suggested null, freshness, format, and referential integrity checks before turning them into live rules.
- Schema-aware quality check recommendations
- Coverage gap detection for new products
- Human review before checks go live
- Feedback tracking on accepted suggestions
Issue Management
When quality checks fail, issues are automatically created with full context — the failing check, affected dataset, historical trend, and suggested remediation. AI triage and impact assessment help stewards prioritize and route work faster.
- Automatic issue creation from check failures
- Kanban board with AI triage suggestions
- AI impact assessment across downstream consumers
- Post-incident debriefs for root cause analysis
Start Monitoring Data Quality Today
Define your first quality check in minutes and know the moment your data doesn't meet expectations.