dvc-azure
azure plugin for dvc
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; Azure credentials must be configured via azure-identity (environment variables, managed identity, or service principal).
- 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 · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute this package freely in commercial and private projects without copyleft obligations.
last release 2026-06-10 (65 days) · last repo commit 2026-08-10 · 3 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 183,774 downloads/mo, #10,059 on PyPI
Alternatives
Verify before relying
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- 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.
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 on it.
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
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.
Requires Python 3.9 or later; Azure credentials must be configured via azure-identity (environment variables, managed identity, or service principal).
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 AzureVerify 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
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdvcadlfsazure-identityknack |
| Maintenance | Actively maintained 65 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 183,774 / month, #10,059 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: dvc_azure-3.1.1-py3-none-any.whl
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See also deeplake · dvc · azure-datalake-store · azureml-pipeline-core · dvc-http · dagster-azure · adlfs · dvc-gdrive · dvc-gs · dvclive