Late-night errors
NULL values slip into production. Dashboards show incomplete data. Your team finds out at 3 AM.
Data Quality
Catch data issues before they become reporting problems.
Monitor important datasets and business-critical metrics with checks, thresholds, and alerts that help teams react early. Catch nullability issues, range violations, pattern mismatches, and custom data anomalies before they impact analytics, dashboards, and reports.
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Enterprise feature included in free trial
The problem with silent data failures
Unexplained spikes
Data anomalies break downstream pipelines. Reports are wrong until someone manually audits.
Missing validation
Without automated checks, bad data propagates silently through analytics and compliance workflows.
Automated data quality checks
Define checks once, run them on schedule, get alerted instantly when issues appear.
Nullability validation
Ensure critical columns never go NULL. Monitor user_id, created_at, order_amount, and anything else that breaks downstream logic.
Range & pattern checks
Validate email formats, phone numbers, percentage ranges, or any value constraints your data must respect.
Custom SQL rules
Write any SQL expression to catch domain-specific anomalies: duplicate IDs, invalid state transitions, business logic violations.
How it works
Three simple steps to catch data issues before they propagate.
1
Define checks
Write simple SQL WHERE clauses that identify bad records. No code or configuration needed.
2
Schedule checks
Attach checks to your query schedules. Validation runs automatically with every export.
3
Get alerts
Receive instant Slack notifications, emails, or webhooks when checks fail.
Who uses data quality checks
Every team that depends on accurate data benefits from automated validation.
Analytics teams
Ensure dashboards and reports display accurate metrics. Catch NULL spikes, missing dimensions, and revenue anomalies.
Data engineers
Validate data at transformation boundaries. Prevent bad data from propagating downstream to BI tools and data lakes.
Compliance teams
Monitor PII masking, audit fields, and regulatory requirements. Detect anomalies that signal data integrity issues.
DataPilot vs alternatives
| Feature | Great Expectations | dbt tests | Custom scripts | DataPilot |
|---|---|---|---|---|
| Works with any SQL database | ✓ | ✗ | ✓ | ✓ |
| No data engineering required | ✗ | ✗ | ✗ | ✓ |
| Real-time Slack alerts | ✗ | ✗ | ✓ | ✓ |
| Works with scheduled exports | ✗ | ✗ | ✓ | ✓ |
| No maintenance or scaling | ✗ | ✓ | ✗ | ✓ |
Built for Enterprise
Data quality checks are included in DataPilot's Enterprise plan with unlimited checks, priority support, and SLA guarantees. Start free today with the Pro plan to evaluate data quality validation.
Questions about data quality checks
Can I define custom validation rules? ▼
Yes. Write any SQL WHERE clause to define data quality rules. You can check for NULL values, invalid ranges, pattern mismatches, or complex business logic violations. Any condition you can express in SQL is a valid check.
How often do checks run? ▼
Checks execute on a schedule you define. Attach checks to query schedules and they run automatically with every execution. You can also trigger checks on-demand or via webhooks.
What databases are supported? ▼
Any SQL database: PostgreSQL, MySQL, SQL Server, Snowflake, BigQuery, Redshift, Oracle, and more. Checks are written in standard SQL so they work across all platforms.
Can I get alerts in Slack? ▼
Yes. Configure Slack alerts to notify your team instantly when a check fails. You also get email notifications and can set up webhooks for custom integrations.
Is this an Enterprise-only feature? ▼
Data quality checks are included in DataPilot's Enterprise plan. Free and Pro trials include full access to evaluate the feature. Contact us for Enterprise licensing details.