YouTube Real-Time Analytics Python API Docs | dltHub

YouTube Analytics API is a REST service that provides access to YouTube channel and video analytics data, including near‑real‑time metrics. The REST API base URL is https://youtubeanalytics.googleapis.com/v2 and All requests require OAuth 2.0 Bearer token 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 YouTube Real-Time Analytics data in under 10 minutes.

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What data can I load from YouTube Real-Time Analytics?

Here are some of the endpoints you can load from YouTube Real-Time Analytics:

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How do I authenticate with the YouTube Real-Time Analytics API?

Include the OAuth 2.0 access token in the Authorization header as Bearer <access_token> for every request.

1. Get your credentials

1. Open the Google Cloud Console (console.cloud.google.com).
2. Create or select a project.
3. In the navigation menu, go to APIs & Services → Library.
4. Search for YouTube Analytics API and click Enable.
5. Navigate to APIs & Services → Credentials.
6. Click Create Credentials → OAuth client ID.
7. Choose Web application (or appropriate type), set authorized redirect URIs, and create.
8. Copy the Client ID and Client Secret.
9. Use an OAuth 2.0 flow (e.g., Authorization Code Grant) to obtain an access token for the required scopes.

2. Add them to .dlt/secrets.toml

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[sources.youtube_real_time_analytics_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 YouTube Real-Time Analytics 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_real_time_analytics_pipeline.py

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

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Pipeline youtube_real_time_analytics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset youtube_real_time_analytics_data
The duckdb destination used duckdb:/youtube_real_time_analytics.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_real_time_analytics_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 reports and group_items from the YouTube Real-Time Analytics 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_real_time_analytics_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://youtubeanalytics.googleapis.com/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "reports", "endpoint": {"path": "reports", "data_selector": "rows"}},
            {"name": "group_items", "endpoint": {"path": "groupItems", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="youtube_real_time_analytics_pipeline",
        destination="duckdb",
        dataset_name="youtube_real_time_analytics_data",
    )
    load_info = pipeline.run(youtube_real_time_analytics_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_real_time_analytics_pipeline").dataset()
sessions_df = data.reports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM youtube_real_time_analytics_data.reports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("youtube_real_time_analytics_pipeline").dataset()
data.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 Real-Time Analytics 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 access token is missing, expired, or does not have the required scopes. Refresh the token or ensure the correct OAuth scopes are granted.

Quota & Rate Limits

• 403 Quota Exceeded – Each request consumes one quota unit. Exceeding the daily quota returns this error; request a higher quota or reduce request frequency.
• 429 Too Many Requests – Indicates transient rate‑limiting. Implement exponential back‑off before retrying.

Invalid Parameters

• 400 Bad Request – Returned when required query parameters are missing or contain invalid values (e.g., unsupported ids or metrics). Verify parameter names and allowed values.

Pagination Issues

• The API uses pageToken for pagination. Omitting or reusing an old token can cause errors; always use the token returned in the previous response.

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