Based on the knowledge sources, this is a known issue with Elementary and the dbt-spark adapter. The error you're seeing where Elementary can't find models in

None.{myschema}

despite having run

dbt run -s elementary

successfully is documented in several community discussions. There are a few potential workarounds you can try: 1. Run with full refresh: Try running

dbt run -s elementary --full-refresh

. This has solved the issue for some users as mentioned in a Slack discussion [Elementary community forum](https://slack.com/archives/C02CTC89LAX/p1702420274487159). 2. Check your dbt_project.yml configuration: Make sure your Elementary configuration in dbt_project.yml is correct:

yaml    models:      elementary:        +schema: "elementary"

3. Add the required flag for newer dbt versions: If you're using dbt 1.8+, you might need to add this flag to your dbt_project.yml:

yaml    flags:      require_explicit_package_overrides_for_builtin_materializations: false

4. Check for materialization overrides: Ensure there aren't any materialization overrides in your dbt_project.yml that might affect Elementary tables. Unfortunately, it's worth noting that dbt-spark is not officially supported by Elementary. As mentioned in a GitHub issue [Elementary GitHub issue](https://github.com/elementary-data/elementary/issues/1441), a collaborator stated: "Unfortunately at this point

dbt-spark

is not an officially supported adapter so we are not actively maintaining this integration, though we do enable community members to contribute fixes there." If these workarounds don't resolve your issue, you might need to consider contributing a fix to the Elementary project or exploring alternative solutions for data observability with your Spark setup. I hope one of these approaches helps resolve your issue! Let me know if you need any clarification or have additional questions about Elementary Data. I'm happy to help you troubleshoot this further based on the information available in the knowledge sources. P.S. The error message format you're seeing is consistent with what other users have reported, which suggests this is a known limitation with the dbt-spark adapter integration.