Load Definitive Healthcare data to DuckDB

Definitive Healthcare is a healthcare intelligence platform providing RESTful APIs for accessing facility, provider, and expert data. Everything needed to build a working Definitive Healthcare → 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 Definitive Healthcare 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 Definitive Healthcare 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 Definitive Healthcare 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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Definitive Healthcare API at a glance

These values come from the Definitive Healthcare API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Definitive Healthcare API?

Authentication requires a POST request to the /v4/token endpoint using application/x-www-form-urlencoded data containing username, password, and grant_type='password'. Once an access token is retrieved, it must be passed in subsequent requests as a Bearer token in the Authorization header.

1. Get your credentials

To obtain API credentials, contact your Definitive Healthcare account representative to request access to the API. Credentials consist of a unique username and password assigned by the company. These are used to authenticate via the OpenID standard by sending a POST request to the /v4/token endpoint with the parameters grant_type=password, your assigned username, and password. The response will provide an access token required for all subsequent data requests.

2. Add them to .dlt/secrets.toml

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[sources.definitive_healthcare_source]
access_token = "REPLACE_ME"

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 Definitive Healthcare data can I load into DuckDB?

These are the Definitive Healthcare endpoints dlt can load into DuckDB:

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How do I load only new Definitive Healthcare records?

The Definitive Healthcare API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "executives", "endpoint": {
    "path": "odata-v4/Executives",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Definitive Healthcare pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v4/token and /v4/odata-v4/Hospitals from the Definitive Healthcare API into DuckDB:

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

@dlt.source
def definitive_healthcare_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.defhc.com/v4/odata-v4",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "executives", "endpoint": {"path": "odata-v4/Executives"}},
            {"name": "news_items", "endpoint": {"path": "odata-v4/NewsItems"}}
        ],
    }
    yield from rest_api_resources(config)


def load_definitive_healthcare_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="definitive_healthcare_pipeline",
        destination="duckdb",
        dataset_name="definitive_healthcare_data",
    )
    load_info = pipeline.run(definitive_healthcare_source())
    print(load_info)


if __name__ == "__main__":
    load_definitive_healthcare_to_duckdb()

Run it with python definitive_healthcare_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 Definitive Healthcare 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("definitive_healthcare_pipeline").dataset()
df = data.executives.df()
print(df.head())

SQL:

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SELECT * FROM definitive_healthcare_data.executives LIMIT 10;

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

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How do I deploy the Definitive Healthcare 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 Definitive Healthcare 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 Definitive Healthcare 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