Load People Data Labs data to DuckDB

People Data Labs provides APIs for enriching, searching, and identifying person and company profiles. Everything needed to build a working People Data Labs → 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 People Data Labs 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 People Data Labs 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 People Data Labs 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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People Data Labs API at a glance

These values come from the People Data Labs API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the People Data Labs API?

Authentication is performed by including your API key either as a query parameter named 'api_key' or in the request header using the key 'X-Api-Key'.

1. Get your credentials

To obtain your People Data Labs API credentials: 1. Navigate to the People Data Labs Dashboard (https://dashboard.peopledatalabs.com). 2. If you do not have an account, sign up at https://www.peopledatalabs.com/signup. 3. Log in to your account. 4. Once on the Home page or in the navigation menu, locate the 'API Keys' section. 5. Copy your 'Active Key' or generate a new one if necessary. Note that this is a secret key and should be kept secure.

2. Add them to .dlt/secrets.toml

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[sources.people_data_labs_source]
api_key = "your_pdl_api_key_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 People Data Labs data can I load into DuckDB?

These are the People Data Labs endpoints dlt can load into DuckDB:

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How do I load only new People Data Labs records?

People Data Labs exposes scroll_token on v5/person/search, 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": "person_search", "endpoint": {
    "path": "v5/person/search",
    "data_selector": "data",
    "incremental": {"cursor_path": "scroll_token", "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 People Data Labs pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading person/enrich and person/search from the People Data Labs API into DuckDB:

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

@dlt.source
def people_data_labs_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.peopledatalabs.com/v5",
            "auth": {"type": "api_key", "api_key": api_key, "name": "X-Api-Key", "location": "header"},
        },
        "resources": [
            {"name": "person_search", "endpoint": {"path": "v5/person/search", "data_selector": "data"}},
            {"name": "person_changelog", "endpoint": {"path": "v5/person/changelog", "data_selector": "updated"}}
        ],
    }
    yield from rest_api_resources(config)


def load_people_data_labs_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="people_data_labs_pipeline",
        destination="duckdb",
        dataset_name="people_data_labs_data",
    )
    load_info = pipeline.run(people_data_labs_source())
    print(load_info)


if __name__ == "__main__":
    load_people_data_labs_to_duckdb()

Run it with python people_data_labs_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 People Data Labs 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("people_data_labs_pipeline").dataset()
df = data.person_search.df()
print(df.head())

SQL:

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SELECT * FROM people_data_labs_data.person_search LIMIT 10;

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

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How do I deploy the People Data Labs 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 People Data Labs 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 People Data Labs 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