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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)
Use either a path to your service account key file or the key contents as a mapping (e.g. from a secret): 
version: 0.1.1
default:
  # Option 1: Path to service account JSON file
  GOOGLE_SERVICE_ACC_KEY_FILEPATH: /path/to/service_account_credentials.json

  # Option 2: Inline credentials (e.g. from env or secrets)
  # GOOGLE_SERVICE_ACC_KEY: "{{ json_value(env_var('GCP_CREDENTIALS_JSON'), 'private_key') }}"
  # Or use mage_secret_var / aws_secret_var etc. for the full credentials object
If the GOOGLE_APPLICATION_CREDENTIALS environment variable is set (e.g. to the path of your service account JSON), you can also use the IO class without adding keys to io_config.yaml; otherwise use with_config as shown below.

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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.google_cloud_storage import GoogleCloudStorage
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_gcs(**kwargs) -> DataFrame:
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'

    bucket_name = '...'  # Change to your bucket name
    object_key = '...'   # Change to your object key (e.g. path/to/file.parquet)

    return GoogleCloudStorage.with_config(
        ConfigFileLoader(config_path, config_profile)
    ).load(bucket_name, object_key)
  5. Run the block.

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Export data to Google Cloud Storage

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

    bucket_name = '...'
    object_key = '...'

    GoogleCloudStorage.with_config(
        ConfigFileLoader(config_path, config_profile)
    ).export(df, bucket_name, object_key)

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Supported formats

Google Cloud Storage supports loading and exporting:
  • .csv
  • .json
  • .parquet

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Permissions

Ensure your Google Cloud service account has the appropriate roles, for example:
  • Storage Object Viewer – to read objects
  • Storage Object Creator – to write objects
  • Storage Object Admin – for full read/write/delete