dagster-azure
Package for Azure-specific Dagster framework op and resource components.
Decision gist · record as of 2026-08-14
Yes, if you are already using Dagster and need to orchestrate workloads on Azure. The package is actively maintained, has no security vulnerabilities, and low install friction. It is a natural choice for teams standardizing on Dagster as their orchestration platform and Azure as their cloud provider. Not necessary if you are not using Dagster or do not have Azure infrastructure.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14).
- Depends on dagster and six Azure SDK packages (azure-core, azure-identity, azure-storage-blob, azure-storage-file-datalake, azure-ai-ml).
- Low friction install with a wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 306,735 downloads/mo, #7,783 on PyPI
Alternatives
Verify before relying
pip install dagster-azure
import dagster as dg
from dagster_azure.adls2 import adls2_resource
@dg.asset
def my_asset() -> str:
return "data"- Specific Azure resource types and operations supported beyond the six runtime dependencies listed.
- Whether this package is the recommended way to integrate Dagster with Azure or if core Dagster handles this natively.
- Performance characteristics and scalability limits when orchestrating large Azure workloads.
What it is and what it does
dagster-azure is a library that bridges Dagster—a cloud-native data pipeline orchestrator—with Microsoft Azure services. It provides pre-built components (ops and resources) for interacting with Azure Blob Storage, Data Lake Storage, and Azure Machine Learning, allowing you to declare data assets and pipelines in Python and have Dagster orchestrate their execution on Azure infrastructure.
The package is part of Dagster's integration ecosystem and is meant to be used alongside the core Dagster framework. It handles the plumbing between your Dagster asset definitions and Azure's SDKs, reducing boilerplate for authentication, connection management, and data movement. It's actively maintained and carries no known vulnerabilities.
Use it for
- Build and orchestrate data pipelines that read from or write to Azure Blob Storage or Data Lake Storage.
- Integrate Azure Machine Learning model training and inference into Dagster-managed data workflows.
- Manage data asset lineage and observability across Azure-based data infrastructure from a single Dagster control plane.
- Develop and test Azure-dependent pipelines locally, then deploy the same code to production without modification.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using Dagster and need to orchestrate workloads on Azure.
The package is actively maintained, has no security vulnerabilities, and low install friction. It is a natural choice for teams standardizing on Dagster as their orchestration platform and Azure as their cloud provider. Not necessary if you are not using Dagster or do not have Azure infrastructure.
Install
dagster-azure on PyPI
Before you install
Low friction install with a wheel distribution. Active maintenance—released 2026-08-14 with 15996 repository stars and no known vulnerabilities.
Requires Python 3.10 or later (supports up to 3.14). Depends on dagster and six Azure SDK packages (azure-core, azure-identity, azure-storage-blob, azure-storage-file-datalake, azure-ai-ml).
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install dagster-azure
import dagster as dg
from dagster_azure.adls2 import adls2_resource
@dg.asset
def my_asset() -> str:
return "data"Verify before relying
- Specific Azure resource types and operations supported beyond the six runtime dependencies listed.
- Whether this package is the recommended way to integrate Dagster with Azure or if core Dagster handles this natively.
- Performance characteristics and scalability limits when orchestrating large Azure workloads.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesazure-ai-mlazure-coreazure-identityazure-storage-blobazure-storage-file-datalakedagster |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 306,735 / month, #7,783 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_azure-0.29.18-py3-none-any.whl
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See also dagster · dagster-aws · dagster-docker · dvc-azure · dagster-gcp · dagster-dg-core · dagster-spark · dagster-rest-resources · dagster-airbyte · dagster-cloud-cli