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Documentation Index

Fetch the complete documentation index at: https://docs.mage.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Add credentials

  1. Create a new pipeline or open an existing pipeline.
  2. Expand the left side of your screen to view the file browser.
  3. Scroll down and click on a file named io_config.yaml.
  4. Enter the following keys and values under the key named default (you can have multiple profiles, add it under whichever is relevant to you)
Required: storage account name. For authentication you can use either Azure AD (service principal) or default credentials (e.g. Azure CLI, managed identity). 
version: 0.1.1
default:
  AZURE_STORAGE_ACCOUNT_NAME: your_storage_account_name

  # Optional: Service principal (if not using DefaultAzureCredential)
  AZURE_CLIENT_ID: ...
  AZURE_CLIENT_SECRET: ...
  AZURE_TENANT_ID: ...
If AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, and AZURE_TENANT_ID are omitted, the client uses DefaultAzureCredential (Azure CLI, environment variables, or managed identity when running in Azure).

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Using Python block

  1. Create a new pipeline or open an existing pipeline.
  2. Add a data loader or transformer block (the code snippet below is for a data loader).
  3. Select Generic (no template).
  4. Enter this code snippet (note: change the config_profile from default if you have a different profile): 
    from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.azure_blob_storage import AzureBlobStorage
from os import path
from pandas import DataFrame

if 'data_loader' not in globals():
    from mage_ai.data_preparation.decorators import data_loader


@data_loader
def load_from_azure_blob(**kwargs) -> DataFrame:
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    container_name = '...'  # Your container name
    blob_path = '...'       # Blob path (e.g. folder/data.parquet)

    return AzureBlobStorage.with_config(
        ConfigFileLoader(config_path, config_profile)
    ).load(container_name, blob_path)
  5. Run the block.

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Export data to Azure Blob Storage

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.azure_blob_storage import AzureBlobStorage
from os import path
from pandas import DataFrame

if 'data_exporter' not in globals():
    from mage_ai.data_preparation.decorators import data_exporter


@data_exporter
def export_data_to_azure_blob(df: DataFrame, **kwargs) -> None:
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    container_name = '...'
    blob_path = '...'

    AzureBlobStorage.with_config(
        ConfigFileLoader(config_path, config_profile)
    ).export(df, container_name, blob_path)

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Supported formats

Azure Blob Storage supports loading and exporting:
  • .csv
  • .json
  • .parquet

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Permissions

Ensure your Azure AD app or managed identity has the appropriate role on the storage account, for example:
  • Storage Blob Data Contributor – read and write blobs
  • Storage Blob Data Reader – read-only