Case Study: Snapcommerce cuts dbt model QA from days to under one day with Datafold
Key results
The challenge
Snapcommerce needed to safely update nearly 500 lines of complex SQL logic for supplier payment processing while handling millions of dollars in weekly transactions across roughly 500 dbt models and 200+ Snowflake source tables. Manual QA was time-consuming and error-prone, requiring finance teams to compare tables by hand and export data to Excel.
The solution
Snapcommerce adopted Datafold's Data Diff, integrated with dbt, to automatically generate comparisons of impacted data across models and enable cross-functional review between the data and finance teams.
“Datafold is like a booster shot... you get extra protection and security when you make changes.”
JTJonathan TalmiSenior Data Platform Manager, Snapcommerce
The results, in context
QA time for updating critical dbt models dropped from 3-4 days to less than one day, roughly a 75% reduction. Snapcommerce reported zero payment-related data incidents and full confidence in the changes it shipped.