dbt_artifacts¶

Tested version: 2.10.1 | Integration tested: Yes (Lakehouse only)

dbt_artifacts persists dbt run metadata (model runs, test results, sources, exposures, seeds, snapshots) into tables in your warehouse so you can query and monitor your project's execution history.

Compute engine support¶

Compute engine	Status
Lakehouse (FabricSpark)	Tested
Data Warehouse (Fabric)	Not compatible — upstream PR brooklyn-data/dbt_artifacts#529 adds Synapse and Fabric DW support

Dispatch configuration¶

dispatch:
  - macro_namespace: dbt_artifacts
    search_order: ['your_project_name', 'dbt', 'dbt_artifacts']
  - macro_namespace: dbt_utils
    search_order: ['your_project_name', 'dbt', 'dbt_utils']

Required project configuration¶

dbt_artifacts must use the delta file format on Lakehouse, otherwise the on-run-end hooks fail to insert rows.

models:
  dbt_artifacts:
    +file_format: delta

Macro compatibility¶

All dbt_artifacts macros work on Lakehouse without adapter-specific overrides. The package targets Spark SQL via dbt-spark's macro implementations, which this adapter inherits.

Notes¶

• Depends on dbt-utils. Include the dbt-utils dispatch configuration alongside the dbt_artifacts one.
• Enable the package's on-run-end upload hooks by adding them to your dbt_project.yml:

on-run-end:
  - "{{ dbt_artifacts.upload_results(results) }}"