It depends a bit on your stack. Out of the box it does a lot with the metadata produced by the tools your using. With something like dbt we can do things like extract your test assertions while for postgres we might use database constraints.

More generally we can embed the transformation logic of each stage of your data pipelines into the edge between nodes (like two columns). Like you said, in the case of SQL there are lots of ways to statically analyze that pipeline but it becomes much more complicated with something like pure python.

As an intermediate solution you can manually curate data contracts or assertions about application behavior into Grai but these inevitably fall out of sync with the code.

Airflow has a really great API for exposing task level lineage but we've held off integrating it because we weren't sure how to convert that into robust column or field level lineage as well. How are y'all handling testing / observability at the moment?