Load Vimeo data to DuckDB

Vimeo is a video hosting, sharing, and services platform that provides a REST API for managing videos, users, and account data. Everything needed to build a working Vimeo → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Vimeo to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Vimeo to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Vimeo API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Vimeo API at a glance

These values come from the Vimeo API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Vimeo API?

API requests require an Authorization header set to 'Bearer {token}' to authenticate. For retrieving tokens/credentials via OAuth, an 'Authorization' header with 'Basic' credentials (base64 encoded client_id:client_secret) is used.

1. Get your credentials

1. Log in to your Vimeo account. 2. Navigate to the Vimeo Developer API site (https://developer.vimeo.com/apps). 3. Click '+Create an app' to register a new application. 4. Once created, select your app from the list to view its dashboard. 5. Retrieve your 'Client Identifier' (Client ID) and 'Client Secret' from the application information page. 6. If needed, you can generate a 'Personal Access Token' directly from the 'Authentication' section in the left-hand navigation of your app's dashboard.

2. Add them to .dlt/secrets.toml

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[sources.vimeo_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
access_token = "your_personal_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Vimeo data can I load into DuckDB?

These are the Vimeo endpoints dlt can load into DuckDB:

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How do I load only new Vimeo records?

Vimeo exposes page on me/videos, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "videos", "endpoint": {
    "path": "me/videos",
    "data_selector": "data",
    "incremental": {"cursor_path": "page", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Vimeo pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /me and /me/videos from the Vimeo API into DuckDB:

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

@dlt.source
def vimeo_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.vimeo.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "videos", "endpoint": {"path": "me/videos", "data_selector": "data"}},
            {"name": "categories", "endpoint": {"path": "categories", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_vimeo_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vimeo_pipeline",
        destination="duckdb",
        dataset_name="vimeo_data",
    )
    load_info = pipeline.run(vimeo_source())
    print(load_info)


if __name__ == "__main__":
    load_vimeo_to_duckdb()

Run it with python vimeo_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Vimeo data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("vimeo_pipeline").dataset()
df = data.videos.df()
print(df.head())

SQL:

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SELECT * FROM vimeo_data.videos LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Vimeo to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Vimeo loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Vimeo data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack