Google Ads Python API Docs | dltHub

Google Ads API is a platform that allows developers to programmatically manage Google Ads campaigns and retrieve advertising data. The REST API base URL is https://googleads.googleapis.com and All requests require an OAuth 2.0 Bearer token for authentication..

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 Ads data in under 10 minutes.

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What data can I load from Google Ads?

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

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How do I authenticate with the Google Ads API?

Requests must include an Authorization header formatted as "Bearer <ACCESS_TOKEN>" where the access token is obtained through the OAuth 2.0 flow.

1. Get your credentials

1. Sign in to the Google Cloud Console (https://console.cloud.google.com).
2. Select or create a project for your Google Ads integration.
3. In the left navigation, go to APIs & Services > Credentials.
4. Click Create credentials > OAuth client ID.
5. Choose Web application (or other appropriate type) and configure authorized redirect URIs if needed.
6. After creation, note the Client ID and Client secret.
7. In the Google Ads API console, enable the Google Ads API for your project and grant the OAuth client the required scopes (e.g., https://www.googleapis.com/auth/adwords).
8. Use the client ID/secret to perform the OAuth 2.0 authorization flow and obtain an access token.
9. Store the access token (or refresh token) for use as the Bearer token in API calls.

2. Add them to .dlt/secrets.toml

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[sources.google_ads_source]
access_token = "your_oauth_access_token_here"

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 Ads 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_ads_pipeline.py

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

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Pipeline google_ads_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_ads_data
The duckdb destination used duckdb:/google_ads.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_ads_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 customers and campaigns from the Google Ads 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_ads_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://googleads.googleapis.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "v12/customers", "data_selector": "results"}},
            {"name": "campaigns", "endpoint": {"path": "v12/customers/{customer_id}/campaigns", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_ads_pipeline",
        destination="duckdb",
        dataset_name="google_ads_data",
    )
    load_info = pipeline.run(google_ads_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_ads_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_ads_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_ads_pipeline").dataset()
data.customers.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 Ads 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 errors

• 401 Unauthorized – Occurs when the Bearer token is missing, expired, or does not have the required scopes. Ensure the OAuth access token is valid and includes the https://www.googleapis.com/auth/adwords scope.

Rate limiting

• 429 Too Many Requests – The API enforces per‑customer and per‑project quotas. Back‑off exponentially and retry after the Retry-After header duration.

Pagination quirks

• Responses contain a nextPageToken when more results are available. Continue fetching by adding the page_token query parameter. Missing or incorrect token values return a 400 Bad Request.

General request errors

• 400 Bad Request – Often caused by malformed query parameters or an invalid page_token.
• 500/503 Server errors – Transient backend issues; retry with exponential back‑off.

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