Data Quality pattern I landed on using dbt + DQX

dbt tests are a great CI gate, but they run after the model builds and only detect by the time one fails, the bad data is already in your table. For "keep the good rows, isolate the bad ones, keep going" pattern, you need row-level DQ that runs in-transit , and I adopted DQX as I use it with other Spark workloads too. The thing I had to unlearn is that you do not rewrite your dbt SQL gold model as Python. The transformation logic stays in a normal `.sql` model, you just add a thin Python model beside it that dbt.ref() s it and applies DQX. The Python model ( orders_gold_dq ) becomes the publis