data-modeling

Describe the bug
We have validation methods that return objects containing Optional collections of issues and things of that nature. In particular, the ModelBuildResult has an optional "issues" tuple which gets populated with a tuple of validation errors whenever validation runs. Making this optional leads to callsite shenanigans like this:

errors = ModelValidator.validate_model.issue

faldbt object already has access to the manifest; we need to pass it here in the exec method. We probably want to pass the dbt manifest directly rather than the fal wrapper as we dont want the fal wrapper to be our public api.

https://github.com/fal-ai/fal/blob/8f17b4f32c96f8d3e2fbb1534ccebca3a9276bcf/src/fal/run_scripts.py#L22-L47

The way it is now:

If collecting commit data fails due to a git checkout error (e.g. permissions error on the repo directory, missing git executable, etc.), the Repo nevertheless gets marked as "Update" and no attempt is made to ever check out the repo again.

The way it should be:

If a worker can't check out a git repo, or can't extract data from it, it should keep retrying until the situ

https://github.com/JPHaus/data-engineering-wiki/blob/main/Tools/Apache%20Spark.md

This note is a seedling and is a great place to make your first contribution!

Describe the bug

Tutorial 1 invites the reader to generate a Turtle file from the YAML example, but the output of the command does not match the output shown in the tutorial (or the data.ttl provided in the repo).

To Reproduce

1. Go to Tutorial 1 and to the corresponding [`tutoria

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How are you using Studio?

Legend Studio

What problems are you trying to solve?

Right now, Studio allows users to make duplication mistakes without any sort of warnings because we rely on engine to throw compilation errors, however, this does not seem to be the case (but that's a separate issue that w