compatibility with new authoring layer #96
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Since this is a dbt cloud focused repo, we should provide the dbt-cloud sqlfluff templater.
Sample files now live in the same s3 bucket as the classic jaffle shop (but in a subdirectory), so that the sharing rules are consistent. We were seeing that the old bucket couldn't be accessed from Snowflake for some reason. I've also added: - a direct download link for each file, so that people can alternatively download the file and then reupload it into their warehouse of choice - the schema for each table to make it easier to manually write create table statements I've validated that all files can be successfully copied into Snowflake and accessed from DuckDB. I recommend using the visual diff: <img width="823" alt="image" src="https://github.com/user-attachments/assets/815b84d4-df40-43c6-9e4d-c16b16f5fa89">
This PR creates a new CODEOWNERS file with a global codeowner to act as a fallback owner. Please reach out to Security Engineering if you have any questions.
Calogica's [dbt-date](https://github.com/calogica/dbt-date) package is no longer supported. Instead, GoDataDriven's [dbt-date](https://github.com/metaplane/dbt-date) package is now featured on the [dbt Package Hub](https://hub.getdbt.com/godatadriven/dbt_date/latest/). This PR migrates the dependency in this repo.
Resolves #80 ### Now Context: dbt-labs/dbt-core#11506 ### Later Once `dbt-core` 1.10.0 is released [here](https://pypi.org/project/dbt-core/#history), then add it back (by either reverting this PR or just adding it back).
Resolves #82 ### Problem Currently, to load example data, the instructions are to edit the `dbt_project.yml` once to enable the `jaffle-data` seeds and then another time to disable them. This is a bit cumbersome for my taste! The repo is explicitly constructed in a way to make it easy to delete all the `jaffle-data` CSVs because we don't want to encourage using dbt as a data loading tool. These seed files only exist to load example data. ### Solution Use `--vars` CLI + `var` macro + `enabled` config to opt-in to loading CSV seeds, but by default do not include them in `dbt build`.
It's weird for the jaffle orders to be all the way back in 2016, so I skipped them forward 8 years ⏩ These are different events to the ones that are in the long term dataset, so it's OK for them to be edited independently of the ones on S3.
Pulling my SL fixes up through main we have a few metrics + dimensions that were not passing the `dbt sl validate` command, and causing issues with downstream consumption! This ensures that all metric queries will compile to valid SQL. <img width="408" alt="image" src="https://github.com/user-attachments/assets/2f9fdca8-c5bb-4e0a-83fb-82d0503b6645" />
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closing in favor of #91 |
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not sure how to do this without breaking possible dependencies