Complete data quality for engineers and their agents
Data Diff in CI/CD, cross-system reconciliation, proactive monitoring — all exposed via MCP so your coding agents can test, debug, and validate programmatically.
Complete data quality for every stage of your pipeline
Catch value-level regressions before production
Automatically compare data before and after every pull request, catching value-level issues that traditional schema tests and row-count checks miss. See exactly which rows and columns changed, why, and whether the change is expected.
Validate data parity across systems
Migration validation, cross-system consistency checks, and SOX compliance — compare billions of rows across databases with value-level precision to ensure your data matches everywhere it needs to.
Proactive anomaly detection
Freshness, volume, and distribution monitoring across your entire data warehouse. Detect quality issues before stakeholders notice — and let your coding agents query monitoring status programmatically via MCP.
Trusted by data teams for testing, monitoring, and reconciliation
"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."
"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 quality across your entire stack
Datafold integrates with Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, GitHub, GitLab, and more — so you can test, reconcile, and monitor data quality wherever your pipelines run.
