Versioning Your Code in Databricks: Best Practices and Tools

Databricks versioning and Git integration in data workflows

Versioning Your Code in Databricks: Best Practices and Tools

Discover the benefits of versioning your work in Databricks. Learn how to turn complex processes into organized, collaborative workflows that enhance the quality of your data projects. By following version control best practices, data professionals can track changes, collaborate more efficiently, avoid code conflicts, and ensure the reproducibility of their data pipelines.

In the world of data engineering, combining powerful version control tools with Databricks’ analytical capabilities can significantly boost team productivity and system reliability. Find out how to implement an effective version control system in Databricks and deliver meaningful results for your data team.

What Is Code Versioning and Why Is It Essential in Databricks?

Code versioning is a system that tracks changes to files over time, allowing you to retrieve specific versions whenever needed. In platforms like Databricks, where notebooks, scripts, and jobs are central to data workflows, versioning becomes even more critical.

Why Is Code Versioning Essential in Databricks?
Why Is Code Versioning Essential in Databricks?

Challenges of Developing Without Version Control in Databricks

Without a proper versioning system in place, teams using Databricks face issues such as:

As one data engineer recently shared: “We lost two full days of work when a production notebook was accidentally overwritten. Version control would’ve saved us that headache”.

Benefits of Proper Version Control

Implementing strong versioning practices brings several key advantages:

Integrating Databricks with Git: The Foundation of Version Control

Connecting Databricks to Git systems like GitHubGitLab, or Azure DevOps lays the groundwork for robust versioning. This integration brings the power of distributed version control to your data workflows.

Setting Up Git Integration in Databricks

To set up Git integration in Databricks, follow these steps:

Here’s an example of how to configure it using the Databricks REST API:

import requests
import json

databricks_instance = “https://seuworkspace.databricks.com”
api_token = “api_token”
headers = {“Authorization”: f”Bearer {api_token}}

git_config = {
“personal_access_token”: “personal_access_token”,
“git_username”: “git_username”,
“git_provider”: “gitHub” # or “gitLab”,
# “azureDevOpsServices”
# “bitbucketCloud”
}

response = requests.patch(
f”{databricks_instance}/api/2.0/git-credentials”,
headers=headers,
data=json.dumps(git_config)
)

print(f”status: {response.status_code})
print(f”response: {response.json()})

Common Git Workflows in Databricks

The most effective Git workflows for Databricks teams include:

Git Flow

Ideal for larger teams with regular release cycles, Git Flow defines specific branches:

GitHub Flow

Simpler and better suited for continuous deployment:

GitLab Flow

Adds environment-specific branches to GitHub Flow:

Git Folders: Native Version Control in the Databricks Platform

Git Folders (formerly known as Databricks Repos) are a native feature that makes it easier to work with Git repositories directly within the Databricks interface, bringing version control into the platform itself.

Key Features of Git Folders

How to Set Up and Use Git Folders

To get started with Git Folders in Databricks:

Once configured, you can:

Organizing Code with Git Folders

An effective structure for organizing data projects within Git Folders might look like this:

project/
|– notebooks/
| |– bronze/ # ingestion and initial validation
| |– silver/ # intermediate transformations
| |– gold/ # analytics layer and modeling
|– conf/ # environment-specific configurations
|– jobs/ # job definitions
|– libraries/ # shared libraries
|– tests/ # automated tests
|– README.md

Best Practices for Versioning in Databricks

Following these practices will ensure a robust and efficient version control process.

Achieving Robust Version Control.
Achieving Robust Version Control.

Repository Structure and Code Organization

Branching Strategies for Different Environments

An effective strategy includes dedicated branches for:

Implement quality gates between environments using pull requests with required approvals.

Code Review and Pull Requests

Your code review process should include:

Semantic Versioning for Libraries and Releases

Adopt semantic versioning (SemVer) for all releases:

CI/CD Integration with Versioning

Integrate your versioning process with CI/CD pipelines:

# example of a CI/CD pipeline for Databricks in Azure DevOps
trigger:
branches:
include:
develop
main

stages:
stage: BuildAndTest
jobs:
job: UnitTests
steps:
script: pytest tests/unit
displayName: ‘Run Unit Tests’

stage: DeployToQA
condition: and(succeeded(), eq(variables[‘Build.SourceBranch’], ‘refs/heads/develop’))
jobs:
job: DeployNotebooks
steps:
task: DatabricksDeployment@0
inputs:
databricksAccessToken: $(databricks_qa_token)
workspaceUrl: ‘https://qa-workspace.databricks.com’
notebooksFolderPath: ‘$(Build.SourcesDirectory)/notebooks’
notebooksTargetPath: ‘/Shared/project-name’

stage: DeployToProd
condition: and(succeeded(), eq(variables[‘Build.SourceBranch’], ‘refs/heads/main’))
jobs:
job: DeployNotebooks
steps:
task: DatabricksDeployment@0
inputs:
databricksAccessToken: $(databricks_prod_token)
workspaceUrl: ‘https://prod-workspace.databricks.com’
notebooksFolderPath: ‘$(Build.SourcesDirectory)/notebooks’
notebooksTargetPath: ‘/Shared/project-name’

Complementary Tools for Versioning in Databricks

To further enhance your version control process, consider using the following complementary tools.

Databricks Notebook Export Utility

This utility allows you to programmatically export notebooks:

import os
from databricks_cli.workspace.api import WorkspaceApi
from databricks_cli.configure.provider import ProfileConfigProvider
from databricks_cli.sdk.api_client import ApiClient

def export_notebooks(source_path, target_dir, format=“SOURCE”):
“””
Exports all notebooks from a Databricks workspace directory to local files.

Args:
source_path: Path of the directory in the Databricks workspace
target_dir: Local directory to save the notebooks
format: Export format (SOURCE, HTML, JUPYTER)
“””

config = ProfileConfigProvider(“DEFAULT”).get_config()
api_client = ApiClient(host=config.host, token=config.token)
workspace_api = WorkspaceApi(api_client)

# list all objects in the directory
objects = workspace_api.list(source_path)

if not os.path.exists(target_dir):
os.makedirs(target_dir)

for obj in objects:
target_path = os.path.join(target_dir, obj.basename)

if obj.object_type == “DIRECTORY”:
# recursively export subdirectories
export_notebooks(obj.path, target_path, format)
elif obj.object_type == “NOTEBOOK”:
# export the notebook
with open(f”{target_path}.py”, “wb”) as f:
content = workspace_api.export(obj.path, format)
f.write(content)
print(f”Exported: {obj.path} -> {target_path}.py”)

# example usage
export_notebooks(“/Shared/Project”, “./backup_notebooks”)

dbx: CLI for Databricks Workflows

dbx is a command-line tool that simplifies:

Basic installation and setup:

pip install dbx

# initialize a new project
dbx init –template jobs-minimal

# deploy a job
dbx deploy —jobs=job_name –environment=dev

Integrations with Package Managers

For shared Python libraries:

# create package structure
mkdir -p mypackage/mypackage
touch mypackage/setup.py mypackage/mypackage/__init__.py

# in setup.py
from setuptools import setup, find_packages

setup(
name=“mypackage”,
version=“0.1.0”,
packages=find_packages(),
install_requires=[
“pyspark>=3.0.0”,
“delta-spark>=1.0.0”
]
)

# build and upload to cluster
python setup.py sdist bdist_wheel
databricks fs cp ./dist/mypackage-0.1.0.whl dbfs:/FileStore/packages/

Collaborative Development with Version Control in Databricks

Proper versioning is the foundation for efficient collaborative development in Databricks.

Conflict Management and Merge

To effectively manage conflicts:

Documentation Integrated with Version Control

Link your documentation to version control:

Training and Versioning Culture

To build a strong versioning culture:

Implementing Version Control in Existing Projects

Migrating existing projects to a version control system requires careful planning.

Migration Strategies

A gradual and safe approach includes:

Case Study: Optimizing a Data Platform in the Financial Sector

An investment management firm transformed its data infrastructure after identifying critical limitations in its new platform. The project focused on three core pillars: performance optimization, robust governance, and reduction of operational costs.

The implemented approach included:

This resulted in measurable outcomes such as:

This transformation highlights how targeted expertise can turn technical challenges into competitive advantages, laying a solid foundation for sustainable growth in the financial sector.

Conclusion: The Future of Version Control in Databricks

Proper code versioning is essential for data teams working with Databricks. With Git integrationDatabricks Repos, and the best practices outlined in this article, your team can collaborate more effectively, securely, and efficiently.

Implementing a robust version control system brings immediate benefits in traceability, code quality, and team productivity. It also lays a strong foundation for adopting advanced practices like Data DevOps and MLOps.

Start versioning your code in Databricks today. Your team will thank you for the clarity, security, and efficiency it brings.

About Indicium

Indicium is a global leader in data and AI services, built to help enterprises solve what matters now and prepare for what comes next. Backed by a 40 million dollar investment and a team of more than 400 certified professionals, we deliver end-to-end solutions across the full data lifecycle. Our proprietary AI-enabled, IndiMesh framework powers every engagement with collective intelligence, proven expertise, and rigorous quality control. Industry leaders like PepsiCo and Bayer trust Indicium to turn complex data challenges into lasting results.

Robson Sampaio is a Data Engineer at Indicium with a background in data engineering, analysis, and visualization. With a degree in Information Technology Management, Robson is driven to use technology and data to create meaningful impact.

Stay Connected

Get the latest updates and news delivered straight to your inbox.

 

United States

119 West 24th St.

New York, NY

Brazil

Avenida Paulista, 1374

São Paulo, SP

Rua Patrício Farias, 131 Florianópolis, SC

Get the latest updates and news delivered straight to your inbox.

© 2025 | Al Rights Reserved by Indicium