Add dbt_artifacts package docs page

Documents the package, dispatch config, required +file_format: delta
setting, dbt-utils dependency, and the on-run-end upload hook. Notes
Lakehouse compatibility (integration-tested) and links to upstream PR
brooklyn-data/dbt_artifacts#529 for Synapse/Fabric DW support.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: sdebruyn

docs/packages/dbt-artifacts.md

@@ -0,0 +1,47 @@
+# dbt_artifacts
+
+**Tested version:** 2.10.1 | **Integration tested:** Yes (Lakehouse only)
+
+[dbt_artifacts](https://github.com/brooklyn-data/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) | :white_check_mark: Tested |
+| Data Warehouse (Fabric) | :x: Not compatible — upstream PR [brooklyn-data/dbt_artifacts#529](https://github.com/brooklyn-data/dbt_artifacts/pull/529) adds Synapse and Fabric DW support |
+
+## Dispatch configuration
+
+```yaml
+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.
+
+```yaml
+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](dbt-utils.md). 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`:
+
+  ```yaml
+  on-run-end:
+    - "{{ dbt_artifacts.upload_results(results) }}"
+  ```
+- For microbatch incremental models that store artifacts over time, configure `partition_by` on the timestamp column (the integration tests partition by `transaction_ts`).

docs/packages/index.md

@@ -61,6 +61,7 @@ The `dbt` entry in the search order tells dbt to check the adapter's built-in ma
 | [dbt-audit-helper](dbt-audit-helper.md) | 0.13.0 | :white_check_mark: Tested | :white_check_mark: Tested |
 | [dbt-external-tables](dbt-external-tables.md) | 0.11.0 | :white_check_mark: Tested | :x: Not applicable |
 | [dbt-profiler](dbt-profiler.md) | 1.0.0 | :white_check_mark: Tested | :white_check_mark: Tested |
+| [dbt_artifacts](dbt-artifacts.md) | 2.10.1 | :x: Not compatible ([upstream PR](https://github.com/brooklyn-data/dbt_artifacts/pull/529)) | :white_check_mark: Tested |
 
 "Tested" means the adapter runs automated integration tests for that package on the given compute engine. "Via dbt-spark" means the package works through inherited dbt-spark macros without adapter-specific overrides.
 

zensical.toml

@@ -43,6 +43,7 @@ nav = [
   ] },
   { "Package support" = [
     { "Overview" = "packages/index.md" },
+    { "dbt_artifacts" = "packages/dbt-artifacts.md" },
     { "dbt-audit-helper" = "packages/dbt-audit-helper.md" },
     { "dbt-codegen" = "packages/dbt-codegen.md" },
     { "dbt-date" = "packages/dbt-date.md" },