daggy VS dbt-expectations

Thanks for the feedback. I'll take a look at how Luigi models task state. Right now each TaskExecutor type is responsible for running and reporting on tasks (e.g. the Slurm executor submits jobs and monitors them for completion). I was considering adding a companion "verify" stage for every vertex, which would be a command that ran and verified output. It might be a way to do what I think you're describing above without having to build in a variety of expected outputs into the daggy core. I'll check what Luigi is doing, though.

> resuming a partially failed build

Daggy does this! Right now it will continue running the DAG until every path is completed or all vertices in a processing state (queued, running, retry, error) are in the error state, then the DAG goes to an error state.

It's possible to explicitly set task/vertex states (e.g. mark it complete if the step was manually completed), then change the DAG state to QUEUED, at which point the DAG will resume execution from where it left off. [1] is a unit test that walks through that functionality.

[1] https://gitlab.com/iroddis/daggy/-/blob/master/tests/unit_se...

Have not used Soda, but dbt indeed is pretty good especially when adding dbt-expectations

dbt-expectations

I highly.. highly.. recommend the dbt-expectations extension from Catologica for dbt. It's a port of Great Expectations, except you can quickly thunk it in your schema.yml's and have it run as part of your dbt test process. Super powerful and it's prevented us from shipping bad data many times.

I'm used to utilising dbt and defining my tests there (along with dbt-utils or https://github.com/calogica/dbt-expectations): I simply add a list item to a column definition and can already define a great number of tests without having to copy code. I can even extend the pre-defined using generic tests. Writing custom tests also integrates nicely. Additionally it's very convenient to tag tests or define a severity. The learning curve for a business engineer is almost flat as long as they know some SQL.

Use dbt's testing functionality during your transformations with catalogica/dbt-expectations (Great Expectations framework ported to dbt)

Check out dbt-expectations https://github.com/calogica/dbt-expectations

There are certain things I would like to add such as data quality, I can use something like dbt great expectations, but I am not sure how much more I should force it before getting an airflow setup..

[dbt Labs employee here]

Check out dbt-expectations package[1]. It's a port of the Great Expectations checks to dbt as tests. The advantage of this is you don't need another tool for these pretty standard tests, and can be early incorporated into dbt workflows.

[1] https://github.com/calogica/dbt-expectations

Also check out dbt-expectations that is a port of Great Expectations that greatly expands the configurable (non-assert) tests.