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The gold standard
in data validation

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Whether you’re migrating, testing, or monitoring, Data Diff ensures your data is accurate, consistent, and ready for every cutover, release, and production run.

Powering leading data teams

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. 

Customers

How our customers are ensuring data quality with speed and confidence

Key metrics
100%+
data accuracy & quality KPI achievement
90%+
faster testing and code review

"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."

Zachary Baustein
Lead Product Analyst
Key metrics
3+
Hours saved during the validation process for each new model
300+
Models rebuilt and validated in Snowflake

"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."

Adam Underwood
Staff Analytics Engineer
Key metrics
200+
HOURS OF TESTING SAVED PER MONTH
20%+
increase in productivity

"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."

John Lee
Director, Product Analytics