dvc-azure

What it is and what it does

dvc-azure is a plugin that extends DVC (Data Version Control) to work with Azure cloud storage services. It allows you to store and version your data, models, and pipeline artifacts in Azure Blob Storage or Azure Data Lake, treating them as remote storage backends just like you would with S3 or GCS. The plugin integrates with DVC's push, pull, and fetch commands to synchronize your versioned assets with Azure.

You install it alongside DVC and configure an Azure storage location as a remote. DVC then handles the rest—tracking which versions of your data are stored where, managing cache, and orchestrating data movement. It's designed for teams running machine learning pipelines or data workflows that already use Azure for infrastructure, letting them keep their data lineage and reproducibility workflows within the DVC ecosystem.

Use it for:

• Store ML model checkpoints and training datasets in Azure Blob Storage, versioned and tracked by DVC alongside your code.
• Set up DVC pipelines that pull raw data from Azure Data Lake, process it, and push results back for team collaboration.
• Manage large artifacts in Azure without duplicating them locally; DVC handles caching and only fetches what you need.
• Integrate DVC-tracked data workflows into Azure ML or other Azure-based MLOps platforms.
• Enable reproducible experiments by pinning dataset versions in DVC while keeping actual data in Azure cloud storage.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Adds Azure Blob Storage and Azure Data Lake support to DVC, enabling version control and pipeline orchestration for data and models stored in Azure cloud services.

Yes, if you use DVC and need Azure storage integration. The plugin is actively maintained, has low install friction, carries no security vulnerabilities, and is licensed permissively. It's in beta status but has been released since 2022 and receives regular updates. Install it only if you're already committed to DVC and Azure; it adds no value standalone.

Install

dvc-azure on PyPI

pip install dvc-azure

uv add dvc-azure

poetry add dvc-azure

Installing dvc-azure

Before you install

Low friction install with active maintenance. Released 65 days ago with recent commits, and depends on four runtime packages (dvc, adlfs, azure-identity, knack) that are standard Azure and DVC ecosystem components.

License in practice

Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute this package freely in commercial and private projects without copyleft obligations.

Quickstart

pip install dvc-azure

import dvc.api
# Configure DVC to use Azure storage as a remote
# dvc remote add myazure azure://container/path
# dvc push/pull to sync with Azure

Requires Python 3.9 or later; Azure credentials must be configured via azure-identity (environment variables, managed identity, or service principal).

Verify before relying

• Whether adlfs, azure-identity, and knack are lightweight or introduce significant transitive dependencies.
• Specific Azure authentication methods supported beyond what azure-identity provides.
• Performance characteristics when working with large datasets on Azure storage.

Package facts

Evidence: dvc_azure-3.1.1-py3-none-any.whl

Keywords: dvc, azure

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