YouTube Reporting Python API Docs | dltHub

YouTube Reporting API is a REST API that lets developers schedule and download bulk YouTube Analytics reports for channels or content owners. The REST API base URL is https://youtubereporting.googleapis.com/v1 and all requests require OAuth 2.0 (Bearer token) with YouTube Analytics/Reporting 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 YouTube Reporting data in under 10 minutes.

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What data can I load from YouTube Reporting?

Here are some of the endpoints you can load from YouTube Reporting:

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How do I authenticate with the YouTube Reporting API?

The API uses OAuth 2.0. Include header Authorization: Bearer <access_token>. Required scopes include https://www.googleapis.com/auth/yt-analytics.readonly and https://www.googleapis.com/auth/yt-analytics-monetary.readonly. For content owners use onBehalfOfContentOwner query parameter.

1. Get your credentials

1. Create or open a project in Google Cloud Console. 2) Enable YouTube Reporting API for the project. 3) Create OAuth 2.0 Client ID credentials (Web or Desktop) and save client_id and client_secret. 4) Use OAuth flow to obtain access_token and refresh_token with scope https://www.googleapis.com/auth/yt-analytics.readonly (and/or https://www.googleapis.com/auth/yt-analytics-monetary.readonly). 5) If acting on behalf of a CMS (content owner), ensure the account has appropriate permissions and include onBehalfOfContentOwner when calling endpoints.

2. Add them to .dlt/secrets.toml

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[sources.youtube_reporting_source]
client_id = "your_client_id.apps.googleusercontent.com"
client_secret = "your_client_secret"
refresh_token = "your_refresh_token"

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 YouTube Reporting 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 youtube_reporting_pipeline.py

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

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Pipeline youtube_reporting_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset youtube_reporting_data
The duckdb destination used duckdb:/youtube_reporting.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 youtube_reporting_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 report_types and jobs_reports from the YouTube Reporting 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 youtube_reporting_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://youtubereporting.googleapis.com/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "report_types", "endpoint": {"path": "reportTypes", "data_selector": "reportTypes"}},
            {"name": "jobs_reports", "endpoint": {"path": "jobs/{jobId}/reports", "data_selector": "reports"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="youtube_reporting_pipeline",
        destination="duckdb",
        dataset_name="youtube_reporting_data",
    )
    load_info = pipeline.run(youtube_reporting_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("youtube_reporting_pipeline").dataset()
sessions_df = data.jobs_reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM youtube_reporting_data.jobs_reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("youtube_reporting_pipeline").dataset()
data.jobs_reports.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 YouTube Reporting 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 / Authorization failures

• 401 Unauthorized: missing/expired/invalid access token. Refresh the token or redo the OAuth flow.
• 403 Forbidden: insufficient OAuth scope (use https://www.googleapis.com/auth/yt-analytics.readonly or yt-analytics-monetary.readonly) or lack of permission for the target resource.

Rate limits & Quotas

• 403 quotaExceeded / userRateLimitExceeded: apply exponential back‑off and verify project quota in Google Cloud Console.

Not found / Invalid IDs

• 404 Not Found: invalid jobId or reportId; ensure IDs come from previous list calls.

Pagination

• List responses are paginated. Use pageToken and pageSize parameters; responses include nextPageToken when more pages exist.

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