without breaking data
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Automated testing for data engineers
Automated testing for data engineers
Don’t wait for stakeholders, monitoring tools, and customers to tell you about broken data.
Adopt a workflow that makes data quality issues a thing of the past.
Trusted by leading data teams, including
Automate proactive testing for all data transformations
with seamless dbt Cloud and Core integrations
See how every code change impacts data
Know exactly what will happen to data and data applications once the code is deployed, right in the pull request. Identify breaking changes, sudden metric shifts and edge cases before they do any damage to the business.
data accuracy & quality KPI achievement
faster testing and code review
of code changes tested before merging to production
“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.”
Lead Product Analyst
Make SQL code reviews a breeze
Stop guessing what this regex does or arguing if that CASE WHEN statement has correct logic. No more custom scripts and audit spreadsheets to fill.
hours saved per month
increase in productivity
pull requests automatically tested per month
"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.”
Director, Product Analytics
Create visibility & stakeholder trust
Stop surprising your data users with unexpected metric changes and broken dashboards. Easily share impact reports with everyone and give heads up before deploying the changes to production.
pull requests checked by Datafold
total operations by Hightouch
Data Quality issues in Production
"With Datafold, we're not just adding trust to our Snowflake instance, we're adding trust to our most important data that is getting activated via Hightouch."
Director of Data Engineering
More building, less toil
Manual data testing is hard, tedious, and error-prone. Focus on what matters and not on writing boilerplate tests, custom scripts and filling out audit spreadsheets.
payment-related data incidents
reduction in QA time, at a higher level of accuracy
“Datafold gives you the ability to QA things in a way that you could almost never do on your own. It’s like a booster shot...you get extra protection and security when you make changes.”
Senior Data Platform Manager
Empower anyone to develop data
With full visibility into every change, everyone, not just data team, can contribute, because testing and reviewing code is so easy!
hours saved during the validation process for each new model
models rebuilt and validated in Snowflake
data team hours will have been saved once the migration is complete
""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."
Staff Analytics Engineer
The fastest way to test data pipelines
Integrates with the modern data stack
Integrations with the most used databases
Deploy and scale securely
Datafold scales to millions of tables, billions of columns and trillions of records
Meeting the highest standards for data security and privacy protection
Deploy Datafold automatically and securely in your AWS or GCP account
Get dedicated support from our team of expert data engineers
Learn more about data testing on our blog
Automate dbt development testing in Snowflake with data-diff
Learn how to test and validate dbt code changes in Snowflake with data-diff. After building your dbt models with 'dbt run' in development you can run the 'data-diff --dbt' command to compare dbt models between environments.
March 23, 2023
How to use dbt-expectations to detect data quality issues
Learn how and when to use dbt-expectations to test dbt sources and models. Avoid alert fatigue by limiting the scope of your tests.
March 17, 2023