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I'm using DBT with a BI tool called Lightdash. Lightdash is a Looker-like BI tool that uses metadata stored under the meta attribute in DBT yaml to act as its configurations. Items defined in the Lightdash configuration also have descriptions that I would like to check for completeness.
I'm using dbt-coverage but would like to extend it to cover Lightdash. I'm planning on forking the dbt-coverage library and adding in checks for Lightdash configs. However, wondering if there would be any way to put those changes back into the main library. I'm thinking maybe a feature to make assertions about stuff under the meta attribbute. I've seen the meta content be used to drive stuff like which team owns a model (and therefore how alerts are routed) and also stuff like which DAG to run the model in (daily vs hourly etc). So there could be value more generally for something like this even for people that don't use Lightdash.
The text was updated successfully, but these errors were encountered:
I'm using DBT with a BI tool called Lightdash. Lightdash is a Looker-like BI tool that uses metadata stored under the
meta
attribute in DBT yaml to act as its configurations. Items defined in the Lightdash configuration also have descriptions that I would like to check for completeness.https://docs.lightdash.com/references/metrics
I'm using dbt-coverage but would like to extend it to cover Lightdash. I'm planning on forking the dbt-coverage library and adding in checks for Lightdash configs. However, wondering if there would be any way to put those changes back into the main library. I'm thinking maybe a feature to make assertions about stuff under the meta attribbute. I've seen the meta content be used to drive stuff like which team owns a model (and therefore how alerts are routed) and also stuff like which DAG to run the model in (daily vs hourly etc). So there could be value more generally for something like this even for people that don't use Lightdash.
The text was updated successfully, but these errors were encountered: