Diff datasets within or across databases, with value-level precision, at any scale
Whether you’re migrating, testing, or monitoring, Data Diff ensures your data is accurate, consistent, and ready for every cutover, release, and production run.
Compare any column, any row, across any database
Data Diff compares datasets at the value level — not just row counts or schema checks. It identifies the exact rows and columns that differ between source and target, across any combination of databases, so you always know precisely what changed and why.
Run and explore data diffs from the UI, API or MCP
Use powerful UI to visualize differences across billions of rows. Supercharge your agentic data engineering workflow with Data Diff as a tool via MCP.
Catch data regressions on every pull request
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.
Data reconciliation for every workflow
Migrate with confidence
When moving from a legacy system to a new platform, Data Diff validates every value
to ensure your target matches your source. This eliminates the guesswork in
cutovers. The result: a migration that’s cheaper, faster, and ready for production
on day one.
Test before you ship
Validate every transformation in CI/CD to see how code changes affect your data and BI assets. Catch discrepancies early, confirm transformations work as intended, and prevent bad data from ever reaching your users. With automated checks in staging, you release faster and with full confidence in your data.
Monitor production data
Continuously validate your most critical tables to catch unexpected changes the moment they happen. With automated monitoring in production, Data Diff protects downstream reports, dashboards, and decisions.
Validate with precision, ship with confidence
Compare any source, any scale
Compare data between any two systems, environments, or points in time. From a
single table to billions of rows, Data Diff handles it with accuracy and speed.
Catch what other tests miss
Data Diff is the gold standard for validating datasets after a code change. Predefined tests and aggregate checks leave critical gaps like missed null values, shifted joins, or silent schema changes. By comparing every value, you uncover discrepancies no other method can.
Let AI do the heavy lifting
AI-assisted code review goes beyond the query and shows you the actual impact on your data. Every pull request highlights the changes that matter so reviewers can focus on outcomes, not syntax. The result: faster reviews, fewer errors, and more time spent shipping.
Automate your checks
Run Data Diff on demand or integrate it into CI/CD pipelines and monitoring schedules to eliminate repetitive QA work and keep projects moving at full speed.
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."
"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."
