> ## 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.

# Google Cloud 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)

Use **either** a path to your service account key file **or** the key contents as a mapping (e.g. from a secret):

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

<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.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.

### Export data to Google Cloud Storage

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

## Supported formats

Google Cloud Storage supports loading and exporting:

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

## 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