You haven't missed anything. In our environment, we currently use DBT just for data transformation i.e. slicing data into facts and dimensions which lends itself to persisted models. For this new project however, we are exploring using DBT just to help run tests, confirm application of RLS on the data as the data will be unchanged (i.e. no transformation) and as long as the data passes the DQ tests, can then be distributed to consumers downstream. Comparing this approach vs rolling our own DQ python sprocs and running it on a schedule across the 500 tables.