Google Drive Python API Docs | dltHub

Google Drive API is a RESTful web service that allows applications to access and manipulate files and folders stored in Google Drive. The REST API base URL is https://www.googleapis.com and all requests require a Bearer token in the Authorization header.

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 add "dlt[hub]" and start loading Google Drive data in under 10 minutes.

---

What data can I load from Google Drive?

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

---

How do I authenticate with the Google Drive API?

Google Drive API uses OAuth 2.0 authentication. Requests must include an Authorization header with the value 'Bearer {access_token}'.

1. Get your credentials

To obtain credentials for the Google Drive REST API, follow these steps in the Google Cloud Console: 1. Navigate to the Google Cloud Console and select or create a project. 2. Open the navigation menu, go to APIs & Services, then select Dashboard. 3. Click + ENABLE APIS AND SERVICES, search for "Google Drive API", and click Enable. 4. Navigate to the Credentials tab in the left-hand menu. 5. Click CREATE CREDENTIALS and select either API key (for public data) or OAuth client ID (for user data). For OAuth 2.0, you must first configure the OAuth consent screen in the same menu. 6. Once created, copy the generated key or download the JSON credentials file for use in your application.

2. Add them to .dlt/secrets.toml

Copy
[sources.google_drive_source]
access_token = "REPLACE_ME"

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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 Drive 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:

Copy
uv run python google_drive_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub show

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

---

Python pipeline example

This example loads files and drives from the Google Drive API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def google_drive_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "files", "endpoint": {"path": "drive/v3/files", "data_selector": "files"}},
            {"name": "changes", "endpoint": {"path": "drive/v3/changes", "data_selector": "changes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_drive_pipeline",
        destination="duckdb",
        dataset_name="google_drive_data",
    )
    load_info = pipeline.run(google_drive_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.

---

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):

Copy
import dlt

data = dlt.pipeline("google_drive_pipeline").dataset()
sessions_df = data.files.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM google_drive_data.files LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("google_drive_pipeline").dataset()
data.files.df().head()

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

---

What destinations can I load Google Drive 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.

---

Next steps

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

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

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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