Load Vercel data to DuckDB

Vercel REST API provides a programmatic interface to manage resources within your Vercel account, including deployments, projects, and authentication tokens. Everything needed to build a working Vercel → 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.

---

Build your Vercel 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 Vercel 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 Vercel 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.

---

Vercel API at a glance

These values come from the Vercel API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Vercel API?

All requests to the Vercel REST API require an Authorization header containing a Bearer token. The token must be included in the header as 'Authorization: Bearer '.

1. Get your credentials

1. Log in to your Vercel dashboard. 2. Navigate to your Personal Account Settings (ensure you are not in a Team view). 3. Go to the Tokens page (typically at /account/tokens). 4. Click Create to generate a new token. 5. Enter a descriptive name for your token, select the appropriate scope, and click Create Token. 6. Copy the generated token immediately, as it will not be displayed again.

2. Add them to .dlt/secrets.toml

Copy
[sources.vercel_source]
token = "your_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.

---

What Vercel data can I load into DuckDB?

These are the Vercel endpoints dlt can load into DuckDB:

---

How do I load only new Vercel records?

Vercel exposes next on v1/feature-flags, 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.

Copy
{"name": "feature_flags", "endpoint": {
    "path": "v1/feature-flags",
    "data_selector": "data",
    "incremental": {"cursor_path": "next", "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.

---

What does the generated Vercel pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v9/projects and /v13/deployments from the Vercel API into DuckDB:

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

@dlt.source
def vercel_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.vercel.com",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "feature_flags", "endpoint": {"path": "v1/feature-flags", "data_selector": "data"}},
            {"name": "deployments", "endpoint": {"path": "v6/deployments", "data_selector": "deployments"}}
        ],
    }
    yield from rest_api_resources(config)


def load_vercel_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vercel_pipeline",
        destination="duckdb",
        dataset_name="vercel_data",
    )
    load_info = pipeline.run(vercel_source())
    print(load_info)


if __name__ == "__main__":
    load_vercel_to_duckdb()

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

---

How do I query Vercel 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):

Copy
import dlt

data = dlt.pipeline("vercel_pipeline").dataset()
df = data.deployments.df()
print(df.head())

SQL:

Copy
SELECT * FROM vercel_data.deployments LIMIT 10;

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

---

How do I deploy the Vercel 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 Vercel loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Vercel 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.

---

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