Data monitoring that starts upstream
Comprehensive data monitoring to prevent downtime and detect data quality issues early.
Your first line of defense against against data quality issues
Keep data aligned across systems
Identify discrepancies between source and target databases down to the exact value. Data diff monitors ensures that your most mission-critical data pipelines maintain ongoing parity, preventing costly data quality issues.
Catch anomalies before they become issues
Leverage ML-powered anomaly detection on key metrics like row count, data freshness, and cardinality, or create custom SQL metrics to suit your needs. With monitoring "moved to the left," your team will stay ahead of potential and costly data quality issues.
Validate data with your own rules
Automate data validation using the rules that matter to your business. Detect and view specific records that fail your tests, such as invalid email addresses or duplicate records, to resolve data quality issues with detail and speed.
Stay ahead of unexpected schema changes
Automatically receive alerts when there are changes to underlying table structure. Be instantly aware of any structural or column-type changes in your data warehouse and prevent unexpected disruptions.
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Real-time alerting
Stay connected and up-to-date about your data quality with integrations to communication platforms like Slack, PagerDuty, Email, and Webhooks. Ensure your team is notified immediately of any anomalies or test failures, and equipped to resolve incidents with urgency.
Built for engineers—and scale
Create and maintain monitors at scale through the Datafold UI, Datafold REST API, or as version-controlled YAML code.
How our customers are ensuring data quality with speed and confidence
"Datafold helps you find the hidden changes you didn't know you made to your data, helping you if they're unintended or understanding what's causing them."
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"Datafold allows real visibility into data changes before the changes are live, reducing mistakes and enabling our analysts and stakeholders to feel confident in their changes."
"You can see right off the bat whether your data quality is what you were expecting, and reviewers can see it, too. Now we’re at the rate where we’re automating code reviews, or close to it, on 100 pull requests per month. And this is just the start."
Data monitoring for your entire stack
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