What I mean by idempotent here is that if the same rows from df1 and df2 get reprocessed after a checkpoint reset, you won't end up with duplicates in the silver table. There are a few ways to handle that:

If you're using foreachBatch with a merge (MERGE INTO), then yeah, that's idempotent by design since the merge logic decides whether to insert or update based on your key columns. Reprocessing the same data just results in matched updates rather than new rows.

If you're doing a plain append with no dedup logic at all, then reprocessing after a checkpoint reset will give you duplicates. In that case you'd want to drop the table too and start clean, or add dedup logic downstream.

So it depends on your write strategy. Merge is the most naturally idempotent, but dropDuplicatesWithinWatermark in append mode also works if you have a reliable event time column and your watermark window is wide enough.