Databricks Project Accelerators
CLI tool that scaffolds production-ready Databricks solutions via Jinja2 templates and Databricks Asset Bundles.
Installation
pip install databricks-project-accelerators
Quickstart
Open an empty folder in VS Code, then run in the terminal:
# See what's available
dpa list
# Scaffold a project
dpa init medallion-sdp
# Open the generated project
code medallion-sdp
cd medallion-sdp
# Authenticate the Databricks CLI if you haven't already
databricks configure
# Deploy to your workspace
databricks bundle deploy
# Run the job
databricks bundle run medallion_sdp_job
That's it — your Databricks solution is live.
Accelerators
| Name | Description |
|---|---|
medallion-sdp |
Streaming Delta Pipeline (DLT) with bronze/silver/gold layers and a DAB job |
medallion-dbt |
Medallion architecture (bronze/silver/gold) using dbt models over TPCH |
mlflow-project |
MLflow training, model registration, and batch scoring over TPCH |
lakebase-streamlit-app |
Databricks App (Streamlit) connected to TPCH analytics + Lakebase master data |
custom-python-wheel |
Custom Python wheel package with a build-and-upload job and an import verification notebook |
ai-bi |
Lakeview dashboard + Genie Space with metric views over the TPCH sample dataset |
CLI reference
dpa init <accelerator> # scaffold a project
dpa init <accelerator> --dry-run # preview files without writing
dpa init <accelerator> --force # overwrite existing files
dpa list # list all accelerators
dpa deploy --env prod # deploy via Databricks Asset Bundle
Requirements
- Python 3.10+
- Databricks CLI v1.3.0+ configured against a Unity Catalog workspace
1 maintainer
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