> ## 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.

# Azure Blob Storage

## 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).

```yaml theme={"system"}
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).

<br />

## 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):

   ```python theme={"system"}
   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.

### Export data to Azure Blob Storage

```python theme={"system"}
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)
```

## Supported formats

Azure Blob Storage supports loading and exporting:

* `.csv`
* `.json`
* `.parquet`

## 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