dbt and Airflow, set up the way teams actually maintain.

Airflow has the larger ecosystem, more managed options and a much bigger hiring pool, which is why we default to it. Dagster has a genuinely better developer experience and treats data assets as first-class, which suits teams with strong engineering practices starting fresh. Either is defensible; the deciding factor is usually who maintains it.

Almost always, and it is usually worth doing. dbt gives you version control, testing, documentation and lineage, none of which stored procedures offer. The migration is mostly mechanical, and the real gain is that transformation logic becomes reviewable in pull requests instead of living undocumented in the database.

Every model should assert that its primary key is unique and not null, and every relationship it depends on should be tested. Beyond that, test the assumptions that would cause a wrong number rather than a failed run, since those are the ones that reach clients. Perfect coverage is not the goal; catching silent wrongness is.

Both, and the training matters more. A dbt project nobody on your team can extend has just moved the bottleneck to us, which is not a good outcome for you. Handover includes the repo, the CI, the docs and working sessions with your analysts.