Azure Data Lake Storage Gen2

​

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)

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: ...

​

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_data_lake_storage import AzureDataLakeStorage
   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_data_lake(**kwargs) -> DataFrame:
       config_path = path.join(get_repo_path(), 'io_config.yaml')
       config_profile = 'default'
   
       container_name = '...'  # File system / container name
       file_path = '...'       # Path to file (e.g. folder/data.parquet)
   
       return AzureDataLakeStorage.with_config(
           ConfigFileLoader(config_path, config_profile)
       ).load(container_name, file_path)
   
5. Run the block.

​

Export data to Azure Data Lake Storage Gen2

from mage_ai.settings.repo import get_repo_path
from mage_ai.io.config import ConfigFileLoader
from mage_ai.io.azure_data_lake_storage import AzureDataLakeStorage
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_data_lake(df: DataFrame, **kwargs) -> None:
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    container_name = '...'
    file_path = '...'

    AzureDataLakeStorage.with_config(
        ConfigFileLoader(config_path, config_profile)
    ).export(df, container_name, file_path)

​

Supported formats

• .csv
• .json
• .parquet

​

Permissions

• Storage Blob Data Contributor – read and write
• Storage Blob Data Reader – read-only