Google Cloud Storage Python API Docs | dltHub

Google Cloud Storage is a RESTful object storage service for storing and retrieving large binary and structured data. The REST API base URL is https://storage.googleapis.com and OAuth 2.0 (Bearer) token required for authenticated requests..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Google Cloud Storage data in under 10 minutes.

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What data can I load from Google Cloud Storage?

Here are some of the endpoints you can load from Google Cloud Storage:

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How do I authenticate with the Google Cloud Storage API?

The JSON API uses OAuth 2.0 Bearer tokens. Include the header Authorization: Bearer <ACCESS_TOKEN> on each request.

1. Get your credentials

1. Open Google Cloud Console. 2) Navigate to IAM & Admin → Service Accounts. 3) Create a new service account and grant it a Storage role (e.g., Storage Object Viewer). 4) Generate and download a JSON key for the service account. 5) Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the path of the JSON key or use the key to obtain an OAuth2 token.

2. Add them to .dlt/secrets.toml

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[sources.google_cloud_storage_source]
service_account_key_path = "/path/to/service-account.json"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Google Cloud Storage API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python google_cloud_storage_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline google_cloud_storage_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_cloud_storage_data
The duckdb destination used duckdb:/google_cloud_storage.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline google_cloud_storage_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads buckets and objects from the Google Cloud Storage API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def google_cloud_storage_source(service_account_key_path=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://storage.googleapis.com",
            "auth": {
                "type": "bearer",
                "token": service_account_key_path,
            },
        },
        "resources": [
            {"name": "buckets", "endpoint": {"path": "storage/v1/b", "data_selector": "items"}},
            {"name": "objects", "endpoint": {"path": "storage/v1/b/{bucket}/o", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_cloud_storage_pipeline",
        destination="duckdb",
        dataset_name="google_cloud_storage_data",
    )
    load_info = pipeline.run(google_cloud_storage_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("google_cloud_storage_pipeline").dataset()
sessions_df = data.objects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_cloud_storage_data.objects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_cloud_storage_pipeline").dataset()
data.objects.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Google Cloud Storage data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

Ensure your OAuth2 access token is valid and not expired. Use Application Default Credentials or a service account JSON key. Include header Authorization: Bearer <ACCESS_TOKEN>. Common error: 401 Unauthorized or 403 Forbidden when permissions are insufficient.

Rate limits and quota errors

Requests may return 429 Too Many Requests or 403 with quota error messages when project quotas are exceeded. Check Cloud Console Quotas and implement exponential backoff and retries.

Pagination and partial responses

List endpoints return nextPageToken when more results exist; pass pageToken to fetch the next page. Use the fields parameter to request partial responses to reduce payload size.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack