Data quality rules
SQL-based business rule validations, unit tests, regression tests, and ad-hoc checks
Data reconciliation for comparing multiple datasets with drag-and-drop column mapping and auto-generated SQL
Percentage-based test cases for proportion-driven quality rules
Trend analysis with configurable deviation thresholds for early anomaly detection
Automated test generation
Standard validations: simple checks for completeness, uniqueness, allowed values, regex, date ranges, data freshness, and more
Natural language to SQL: describe a data quality rule to generate a test case automatically
Validation suggestions based on metadata and profiling history
Reusable SQL templates with custom parameters for bulk test creation
Monitoring & alerting
- Schedule-based or API-triggered executions to validate data at every pipeline stage
Customized alerting via email or webhook (Slack, Teams, Jira, and more)
Row-level execution diffs showing newly introduced, resolved, and recurring failures across runs
Full change history and audit trail for every test case
Dashboards & reporting
Role-based dashboards combining test suites, reports, business rules, and data objects in one view
Flexible layouts and visualizations tailored to each team or business domain
Custom reports to build targeted quality metrics (e.g. data quality dimension, domain, and more)
Shareable dashboards for communicating data quality status to stakeholders
