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