The legacy pandas pipeline processed 200,000 rows of sales data daily. It took 45 minutes to run and crashed twice a week. The business wanted PySpark for speed and reliability.
We ran Fabric-Pandas-Migrator in review-first mode. It identified the 47 functions that needed to change, showed the proposed diff for each one, and waited for our approval before applying anything.
Three of the functions had subtle pandas-specific behaviour that didn't translate cleanly to PySpark. We caught those before they became production bugs. The migration took two days instead of the estimated three weeks of manual rewriting.
The pipeline now runs in eight minutes and hasn't crashed in six months.