Case Study: Nextbite saves ~3 hours a week with Sifflet monitoring
Key results
The challenge
Nextbite, an all-in-one virtual restaurant company, relied on manual dbt/SQL tests and business partners to surface data provider issues. Errors were often caught days later and required extensive custom rule development.
The solution
Nextbite deployed Sifflet and applied roughly 80 advanced monitoring rules, using machine-learning-based monitoring and data lineage to detect and trace data provider issues in real time.
“We chose Sifflet for its wide offering. We had checked out other vendors in the space, and they have all data quality rules, but what we need is the next step. Being able to know what to do once that rule fails and ensure it's resolved.”
RSRoss ServenDirector of Data Engineering, Nextbite
The results, in context
Nextbite deployed around 80 advanced monitoring rules, saving several weeks of custom SQL/dbt development, including a single completeness rule that had previously taken 40+ hours to build and test. The team saves about 3 hours per week no longer manually tracking data errors, and Sifflet identifies 1-2 data provider issues per week on the day they occur.