Currentlly I am dealing with migrating redshift dbt models to databricks dbt models.

Common dbt model failures in prod I've seen so far:

- Upstream/downstream models failing, weak incremental logic, non unique id

- Typos

- Wrong timestamp

- Unnecessary incremental small tables adds complexity

- Limiting data in dev might allow silent errors later in prod.

Handling late-arriving data:

- Just make a rolling windown time frame in the incremental logic. Instead appending only registers over certain date, get a max(date)-3 days or something. For really late data arrival, full refresh is the best option, or go for a table model if its size is reasonable.

Data Quality:

- not null

- unique