# DRE as a Databricks job | DRE

> Install DRE from a wheel in a serverless job and write the report to a Volume.

[← All use cases](https://getdre.com/#use-cases)

# DRE as a Databricks job

You already run your pipelines on Databricks. DRE installs from a Python wheel inside a Databricks job, reads from your warehouse, and writes the result straight to a Unity Catalog Volume, where the volume is mounted on the job’s compute.

Recorded from a real run on Databricks serverless: the job in the Databricks UI, then the file it wrote to a Volume.

Recorded from a real run on Databricks serverless: the job in the Databricks UI, then the file it wrote to a Volume.

![The DRE demo job in Databricks Jobs & Pipelines, with its run history and succeeded runs](https://getdre.com/media/databricks_job/run.webp)

The job in Databricks (Jobs & Pipelines), cropped to the run history.

![The Volume folder dre\_demo/from\_job/duckdb in Databricks Catalog Explorer, listing regions-20261001.xlsx](https://getdre.com/media/databricks_job/volume.webp)

What the job wrote: a Volume folder in Catalog Explorer, cropped to the file listing.

databricks.yml (the job)

```yaml
resources:
  jobs:
    dre_reports:
      name: "DRE demo: reports to a Volume"
      description: Installs DRE from the dre-cli wheel and runs the project's `job` reports.
      max_concurrent_runs: 1
      tasks:
        - task_key: run_dre
          environment_key: dre
          spark_python_task:
            python_file: ./bundle/run_dre.py
            parameters:
              - --root
              - ${workspace.file_path}
              - --select
              - "tag:job"
              - --set
              - all
              - --http-path
              - ${var.warehouse_http_path}
              - --volume
              - ${var.volume}
              - --run-date
              - ${var.run_date}
              - --target-check
              - ${var.target_check}
              - --plugins-dir
              - ${var.plugins_dir}
      environments:
        - environment_key: dre
          spec:
            client: "2"
            dependencies:
              - ${var.wheel}
              - databricks-sdk>=0.40
```

reports/job/job\_duckdb\_to\_volume.yml

```yaml
# Run by the Databricks job (bundle/): DuckDB data shipped with the job, written to the Volume.
# On Databricks compute the volume is mounted, so DRE copies to /Volumes directly.
queries:
  - {query: job_region_summary, tab_name: Regions}
  - {query: job_run_facts, tab_name: Run}
output:
  format: xlsx
  destination:
    profile: vol_auto
    path: "{{ env_var('DATABRICKS_VOLUME', '/Volumes/workspace/career_vista/dre_output') }}/dre_demo/from_job/duckdb/regions-{{ run.date.yyyymmdd }}.xlsx"
```

terminal

```bash
databricks bundle deploy -p DEFAULT --var "wheel=./dist/dre_cli-0.1.0-py3-none-manylinux_2_35_aarch64.whl"
databricks bundle run dre_reports -p DEFAULT --var "wheel=./dist/dre_cli-0.1.0-py3-none-manylinux_2_35_aarch64.whl"
```

### What to notice

-   Databricks is both a source and a destination (Volumes and workspace files).
-   Sign in with OAuth, a personal access token or a service principal.
-   DRE doesn’t need its own scheduler: Databricks Jobs, Airflow, Dagster or cron call dre run.

### In the docs

-   [Plugins →](https://getdre.com/docs/plugins/)