prefect-azure

PyPI

prefect-azure is a collection of Prefect integrations for orchestration workflows with Azure.

Getting Started

Installation

Install prefect-azure with pip

pip install prefect-azure

To use Blob Storage:

pip install "prefect-azure[blob_storage]"

To use Cosmos DB:

pip install "prefect-azure[cosmos_db]"

To use ML Datastore:

pip install "prefect-azure[ml_datastore]"

Managed identity authentication for the Prefect server database

prefect-azure provides a Prefect plugin that lets the Prefect server connect to Azure Database for PostgreSQL using a Microsoft Entra ID (managed identity) token instead of a password. When enabled, the server acquires a short-lived Entra token via DefaultAzureCredential and supplies it to asyncpg on every new connection, so tokens refresh automatically and no database password is stored anywhere.

Enable it on the process running prefect server start (install prefect-azure in that image/environment):

# Enable the plugin system so Prefect loads the database hook
# (on Prefect < 3.7 use PREFECT_EXPERIMENTS_PLUGINS_ENABLED=true instead)
export PREFECT_PLUGINS_ENABLED=true

export PREFECT_INTEGRATIONS_AZURE_POSTGRES_MANAGED_IDENTITY_ENABLED=true
# Optional: select a specific user-assigned identity
export PREFECT_INTEGRATIONS_AZURE_POSTGRES_MANAGED_IDENTITY_CLIENT_ID=<client-id>

# Provide a password-less connection URL (the plugin supplies the token)
export PREFECT_SERVER_DATABASE_CONNECTION_URL="postgresql+asyncpg://<entra-principal>@<host>:5432/<db>"

The Postgres server must have Microsoft Entra authentication enabled and the identity mapped to a database role (via pgaadauth). Locally, DefaultAzureCredential falls back to your az login identity, so the same configuration works for development.

Examples

Download a blob

from prefect import flow

from prefect_azure import AzureBlobStorageCredentials
from prefect_azure.blob_storage import blob_storage_download

@flow
def example_blob_storage_download_flow():
    connection_string = "connection_string"
    blob_storage_credentials = AzureBlobStorageCredentials(
        connection_string=connection_string,
    )
    data = blob_storage_download(
        blob="prefect.txt",
        container="prefect",
        azure_credentials=blob_storage_credentials,
    )
    return data

example_blob_storage_download_flow()

Use with_options to customize options on any existing task or flow:

custom_blob_storage_download_flow = example_blob_storage_download_flow.with_options(
    name="My custom task name",
    retries=2,
    retry_delay_seconds=10,
)

Azure Container Instance Worker

Use the Azure Container Instance worker to run flow runs in Azure Container Instances.

To get started, create an Azure Container Instances typed work pool:

prefect work-pool create --type azure-container-instance my-aci-work-pool

Then, run a worker that pulls jobs from the work pool:

prefect worker start --pool my-aci-work-pool --type azure-container-instance

Project links

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Credits

Author: Prefect Technologies, Inc.

GitHub Statistics

Data verified by PyPI on Sep 8, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache License 2.0)

Requires

Python >=3.10

Provides Extra

blob-storage cosmos-db ml-datastore all-extras

Tags

prefect

Classifiers

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