---
id: databricks-bundles
version: "1.12.1"
license: unclear
license_treatment: unclear
maintenance: active
---

# databricks-bundles — Python support for Declarative Automation Bundles

License: unclear · Maintenance: active · Downloads: 985.3K/mo

## What it is and what it does

databricks-bundles is a Python extension for Databricks Declarative Automation Bundles that lets you define, generate, and modify Databricks jobs and pipelines using Python code instead of (or alongside) YAML. It integrates with the Databricks CLI to manage bundle deployment and supports dynamic job creation from metadata, making infrastructure-as-code workflows more flexible and programmatic.

The package requires Python 3.10 or later and the Databricks CLI 1.12.1 or above as an external prerequisite. It has no runtime dependencies beyond the CLI itself, making it lightweight to install. Use it when you need to automate job definitions, apply conditional logic to pipeline configurations, or manage Databricks workflows as code within a Python-first development workflow.

Use it for:

- Define Databricks jobs and pipelines as Python code instead of YAML for programmatic control and version management
- Generate jobs and pipelines dynamically from metadata or configuration without manual YAML editing
- Coexist Python-defined jobs alongside existing YAML-defined jobs in the same bundle
- Modify job and pipeline configurations during bundle deployment based on environment or runtime conditions

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Extends Databricks Declarative Automation Bundles to define jobs and pipelines as Python code, dynamically generate them from metadata, and modify bundle definitions during deployment.

Yes, if you are already using Databricks and need to define jobs and pipelines programmatically. The package is actively maintained, has low install friction, and fills a clear gap for Python-based infrastructure-as-code on Databricks. The unclear license status warrants verification before adoption in proprietary projects, and you must have the Databricks CLI 1.12.1+ installed and authenticated first.

## Install

pip install databricks-bundles
uv add databricks-bundles
poetry add databricks-bundles

## Installing databricks-bundles

Before you install: Low install friction with a pure Python wheel. Active maintenance with a release 2 days old. Requires Databricks CLI version 1.12.1 or above as a prerequisite external tool.

License in practice: License treatment is unclear; the package itself has no declared SPDX license, though use is subject to the Databricks License and Privacy Notice per the documentation.

Quickstart:

# Install Databricks CLI 1.12.1 or above, then:
pip install databricks-bundles

# Authenticate and initialize a bundle:
databricks configure
databricks bundle init pydabs

# Define jobs/pipelines in Python within the bundle project

Requires Databricks CLI version 1.12.1 or above installed and workspace authentication configured via `databricks configure`

Verify before relying:

- Whether the package is published under an open-source license or proprietary terms
- Compatibility with Databricks CLI versions beyond 1.12.1
- Support for Python versions older than 3.10

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 985.3K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

databricks jobs as python code, declarative automation bundles python, databricks pipeline definition, databricks bundle deployment, python databricks workflows, databricks-integration, infrastructure-as-code

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