YouTube Python API Docs | dltHub

YouTube Data API is a RESTful service that enables integration of YouTube features such as videos, playlists, and channels into external applications. The REST API base URL is https://www.googleapis.com/youtube/v3 and Requests must include an API key parameter or 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 YouTube data in under 10 minutes.

---

What data can I load from YouTube?

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

---

How do I authenticate with the YouTube API?

You can authenticate by adding your API key to the key query parameter, or by including an OAuth 2.0 access token in the Authorization: Bearer <token> header for requests that require user authorization.

1. Get your credentials

1. Go to the Google Cloud Console (https://console.cloud.google.com).\n2. Create or select a project.\n3. In the navigation menu, choose APIs & Services → Library and enable YouTube Data API v3.\n4. After enabling, go to APIs & Services → Credentials.\n5. Click Create credentials → API key.\n6. Copy the generated API key and store it securely; you will use it as the key query parameter.

2. Add them to .dlt/secrets.toml

Copy
[sources.youtube_data_source]
api_key = "your_api_key_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 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:

Copy
python youtube_data_pipeline.py

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

Copy
Pipeline youtube_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset youtube_data_data
The duckdb destination used duckdb:/youtube_data.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline youtube_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.

---

Python pipeline example

This example loads videos and channels from the YouTube API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def youtube_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com/youtube/v3",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "videos", "endpoint": {"path": "videos", "data_selector": "items"}},
            {"name": "channels", "endpoint": {"path": "channels", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="youtube_data_pipeline",
        destination="duckdb",
        dataset_name="youtube_data_data",
    )
    load_info = pipeline.run(youtube_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.

---

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):

Copy
import dlt

data = dlt.pipeline("youtube_data_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM youtube_data_data.search LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("youtube_data_pipeline").dataset()
data.search.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load YouTube 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.

---

Troubleshooting

Authentication errors

• 401 Unauthorized – The API key is missing or invalid. Verify that the key query parameter is correct and not expired.
• 403 Forbidden – The OAuth token does not have the required scope or the key does not have access to private data.

Quota and rate‑limit errors

• 403 Quota Exceeded – The request exceeded the daily quota. Check the quota usage in the Google Cloud Console and request a higher quota if needed.

Pagination quirks

• The API uses pageToken for pagination. Include the nextPageToken from the response in the subsequent request to retrieve additional pages.

Specific endpoint errors

• playlistItemsNotAccessible (403) – The caller does not have permission to view the playlist items.
• playlistNotFound (404) – The specified playlist ID does not 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.

---

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.

Copy
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