A slow MERGE in Snowflake usually comes down to one of three things:

1. Scanning everything. If the source is the whole staging table, every run reprocesses history. Filter on a high-water mark (updated_at > last load) so you only merge new rows.

2. Duplicate keys in the source. When several source rows match one target row, the MERGE either errors or does extra work. Deduplicate first with QUALIFY ROW_NUMBER() = 1.

3. No pruning on the target. Snowflake can only skip micro-partitions when the ON or filter conditions touch a column that the target is clustered or naturally ordered by, such as a date. Adding a date-range condition can cut the scan dramatically, as long as late updates can't fall outside that window.

Fix these three and you'll often see a MERGE that took minutes finish in seconds.

#Snowflake #SQL #DataEngineering