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The PySpark job runs fine. Somebody still has to patch, size, and babysit the cluster underneath it.
What you'll have at the end
The same transform running as a Snowpark job inside Snowflake: its regional totals checked against the old PySpark job's own output, running on a schedule of its own, with no cluster left to patch or resize.
You need
A working PySpark transform that reads raw order rows, applies a Python business rule, groups and sums a total, and writes it to a table a dashboard already reads, running on a scheduled cluster, plus a Snowflake role able to create warehouses, functions, procedures and tasks in the target database.
Not covered
Porting a whole codebase or its scheduler configuration at once; this covers one transform's own read, business rule, and write, ported and proven on its own.
Give a new Snowflake warehouse an auto-suspend before it idles
the warehouse this recipe opens still needs its own auto-suspend setting decided, beyond picking a size, before it becomes the thing quietly billing overnight
Is a resource monitor stopping your next runaway Snowflake bill?
once this transform runs on a schedule instead of a cluster somebody watches by hand, a credit cap is what stops a stuck or noisy run from billing unwatched
Checked 26 Aug 2026
Part of the Snowflake Data Cloud cookbook