Datafold Case Studies

"There is some sort of fear, if you have a lot of [dbt] models, to break something that you’re not aware of. Datafold makes this ‘fear to merge’ completely go away, because you can check the downstream dependencies for issues."

"I typically had to schedule one to two weeks of testing without Datafold Cloud, that is manual ad-hoc queries to check counts, null values, primary keys between dev and production. I was at a breaking point with how much wasted time and energy this was causing. I’m glad we got Datafold Cloud just in time!"

"Datafold encourages good behavior. We value moving fast over doing things absolutely perfectly the first time. So we would just not do the right thing. Datafold helps us do the right thing."

"Datafold's column-level lineage gives confidence in the whole system. If my stakeholders ask “why is this dashboard out of date?” I can answer in 25 seconds instead of digging through pull requests for hours. As a product owner, I can understand how the rest of the company makes decisions based on the data we produce. It brings data confidence and visibility to the company."

"Datafold's Data Diff is the missing piece of the puzzle for data quality assurance. When I first heard about Datafold, all I could think was “Finally!” - it’s an unspoken problem that we all know about and no one wants to talk about. Datafold gives us full confidence when we ship changes into production, which historically I couldn’t say about PRs."

"Datafold’s Data Diff is great for scaling the PR review process. It creates consistency across every data engineer - instead of each person having their own ideas about how to evaluate a PR, this makes sure every PR is evaluated in the same capacity."

"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."

"We front loaded the validation to be on the model level, when those things are reliable, everything downstream becomes trustworthy. This made it much easier to debug if a report looks weird. It means that the problem is most certainly on the Mode side or in the code generating the report instead of the datasets."

"Datafold is like a booster shot... you get extra protection and security when you make changes."

"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. Data that's directly driving decisions, sending emails, and interfacing with our customers."

"We have great analysts across the technical spectrum which was one of the reasons we looked into Datafold. We want to give our analysts the ability to make their own dbt transformations. 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."