Google Admin Reports Python API Docs | dltHub

The Admin SDK Reports API is a REST API that provides audit and usage reports for Google Workspace accounts. The REST API base URL is https://admin.googleapis.com and all requests require OAuth 2.0 authorization with appropriate Admin SDK Reports scopes.

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 Admin Reports data in under 10 minutes.

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What data can I load from Google Admin Reports?

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

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How do I authenticate with the Google Admin Reports API?

The API uses OAuth 2.0; requests must include an access token in the Authorization header as 'Authorization: Bearer {access_token}'. Use service account or delegated domain-wide authority for admin-level access.

1. Get your credentials

1. In Google Cloud Console create a new project (or select an existing one). 2) Enable the Admin SDK (Reports API) for the project. 3) Create OAuth 2.0 credentials: for server-to-server use, create a service account and generate a JSON key; for delegated admin access enable domain-wide delegation and record the client ID. 4) Grant the service account (client ID) domain-wide authority in Google Workspace Admin console and add required scopes (e.g. https://www.googleapis.com/auth/admin.reports.audit.readonly and https://www.googleapis.com/auth/admin.reports.usage.readonly). 5) Exchange JWT for access tokens and include Bearer token in API calls.

2. Add them to .dlt/secrets.toml

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[sources.google_admin_reports_source]
service_account_json = "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 Admin Reports 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_admin_reports_pipeline.py

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

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Pipeline google_admin_reports_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_admin_reports_data
The duckdb destination used duckdb:/google_admin_reports.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_admin_reports_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 activities and user_usage_report from the Google Admin Reports 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_admin_reports_source(service_account_json=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://admin.googleapis.com",
            "auth": {
                "type": "bearer",
                "token": service_account_json,
            },
        },
        "resources": [
            {"name": "activities", "endpoint": {"path": "admin/reports/v1/activity/users/{userKey}/applications/{applicationName}", "data_selector": "items"}},
            {"name": "user_usage_report", "endpoint": {"path": "admin/reports/v1/usage/users/{userKey}/dates/{date}", "data_selector": "usageReports"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_admin_reports_pipeline",
        destination="duckdb",
        dataset_name="google_admin_reports_data",
    )
    load_info = pipeline.run(google_admin_reports_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_admin_reports_pipeline").dataset()
sessions_df = data.activities.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_admin_reports_data.activities LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_admin_reports_pipeline").dataset()
data.activities.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 Admin Reports 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

If your requests return 401/403, verify the OAuth2 access token and scopes. For domain-wide service accounts ensure domain-wide delegation is enabled and the client ID has the Reports scopes granted in Admin Console. Include 'Authorization: Bearer {token}' header.

Pagination and selectors

Most list endpoints return paginated results with nextPageToken. Use pageToken and maxResults to paginate. Activity list responses place records under the 'items' array; usage report GETs return 'usageReports' containing report entries.

Rate limits and quota errors

Google APIs return 403 with quotaExceeded or 429 for rate limit conditions. Implement exponential backoff and retry on 429/503/500.

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