Google Analytics Data Python API Docs | dltHub

Google Analytics Data API is a REST API that provides programmatic access to Google Analytics 4 (GA4) report data. The REST API base URL is https://analyticsdata.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 Analytics Data data in under 10 minutes.

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What data can I load from Google Analytics Data?

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

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How do I authenticate with the Google Analytics Data API?

The Google Analytics Data API uses OAuth 2.0 for authentication. Requests must include an Authorization: Bearer <ACCESS_TOKEN> header, and common scopes include https://www.googleapis.com/auth/analytics.readonly.

1. Get your credentials

To obtain API credentials for the Google Analytics Data API, you typically need to set up an OAuth 2.0 client ID or a service account in the Google Cloud Console. For a service account, create a new service account, generate a JSON key file, and grant it the necessary Google Analytics permissions (e.g., Google Analytics Data API Viewer). For OAuth 2.0 client IDs, configure consent screen and credentials, then use the client ID and client secret to obtain an access token through an OAuth flow.

2. Add them to .dlt/secrets.toml

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[sources.google_analytics_data_source]
access_token = "your_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 Analytics Data 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_analytics_data_pipeline.py

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

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Pipeline google_analytics_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_analytics_data_data
The duckdb destination used duckdb:/google_analytics_data.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_analytics_data_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 metadata and run_report from the Google Analytics Data 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_analytics_data_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://analyticsdata.googleapis.com",
            "auth": {
                "type": "bearer",
                "access_token": access_token,
            },
        },
        "resources": [
            {"name": "metadata", "endpoint": {"path": "v1beta/properties/{property_id}/metadata"}},
            {"name": "run_report", "endpoint": {"path": "v1beta/properties/{property_id}:runReport", "data_selector": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_analytics_data_pipeline",
        destination="duckdb",
        dataset_name="google_analytics_data_data",
    )
    load_info = pipeline.run(google_analytics_data_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_analytics_data_pipeline").dataset()
sessions_df = data.run_report.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_analytics_data_data.run_report LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_analytics_data_pipeline").dataset()
data.run_report.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 Analytics Data 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 and Authorization Errors

• 401 Unauthorized: This error indicates that your request is missing a valid OAuth token or the token provided is invalid. Ensure your Authorization: Bearer <ACCESS_TOKEN> header is correctly formatted and the access token has not expired.
• 403 Forbidden: This error typically means the authenticated user or service account lacks the necessary permissions to access the requested Google Analytics 4 property or the Google Analytics Data API is not enabled for your Google Cloud project. Verify that the correct scopes are granted (e.g., https://www.googleapis.com/auth/analytics.readonly) and the API is enabled in the Google Cloud Console.

Quota and Rate Limit Errors

• 429 Too Many Requests: This error occurs when you exceed the API's quota or rate limits. Implement exponential backoff for retries and review your project's quota usage in the Google Cloud Console.

Bad Request Errors

• 400 Bad Request: This error signifies an invalid request body, such as incorrect dimension or metric combinations, or malformed JSON. Review the API documentation for the specific endpoint to ensure your request parameters and body adhere to the required format and constraints.

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