Load Vimeo data to PostgreSQL

Vimeo is a video platform with a REST API for accessing and managing videos, users, showcases, and related resources. Everything needed to build a working Vimeo → PostgreSQL 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 PostgreSQL pipeline

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

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These values come from the Vimeo API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Vimeo API?

Send the OAuth 2 access token in the HTTP Authorization header as bearer {access_token}. Vimeo's documentation also shows Accept: application/vnd.vimeo.*+json;version=3.4 for API-version negotiation; requests that create or update JSON resources use Content-Type: application/json. The token is an opaque access-token string issued by Vimeo, not a page number or a documented fixed-format token.

1. Get your credentials

1. Sign in to Vimeo and open the Vimeo Developer Site/My Apps page.
2. Select “+ Create an app,” enter an app name and description, choose whether other Vimeo accounts may access it, accept the API License Agreement and Terms of Service, and select “Create app.”
3. Open the app’s management page. It contains the app’s client_id and client_secret and the option to create personal access tokens.
4. In the app’s left navigation, choose “Generate Access Token.” Select “Authenticated (you)” to access your account’s private metadata, or “Unauthenticated” for public metadata only. For an authenticated token, select the required OAuth scopes (the private scope is required for private data), then select “Generate.”
5. Copy the displayed token immediately and store it securely. Send it on API requests as Authorization: bearer YOUR_ACCESS_TOKEN. Vimeo uses OAuth 2.0 bearer tokens; for a personal access token, client_id/client_secret are not needed in each API request. If another user must authorize your application, use an OAuth flow instead of a personal token.

2. Add them to .dlt/secrets.toml

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[sources.vimeo_source]
api_token = "your_vimeo_personal_access_token"

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 PostgreSQL?

These are the Vimeo endpoints dlt can load into PostgreSQL:

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How do I load only new Vimeo records?

Vimeo exposes modified_time 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": "modified_time", "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/videos and /videos/{video_id} from the Vimeo API into PostgreSQL:

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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": "user_videos", "endpoint": {"path": "users/{user_id}/videos", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


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


if __name__ == "__main__":
    load_vimeo_to_postgres()

Run it with uv run 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 PostgreSQL?

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 PostgreSQL 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