Load Veeva Vault data to DuckDB

Veeva Vault REST API is a platform API for managing content, metadata, and business processes within Veeva Vault applications. Everything needed to build a working Veeva Vault → 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 Veeva Vault 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 Veeva Vault 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 Veeva Vault 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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Veeva Vault API at a glance

These values come from the Veeva Vault API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Veeva Vault API?

All authenticated requests require an 'Authorization' HTTP header, which must contain either a session ID or a 'Bearer' token (API access token). When using a session ID directly, provide it as the header value, or optionally prefix it with the 'Bearer' keyword.

1. Get your credentials

Veeva Vault recommends using an API Access Token for authentication. If you do not have one, you must first generate a session ID by sending a POST request to the '/api/{version}/auth' endpoint with your 'username' and 'password' as 'x-www-form-urlencoded' parameters. Once you have a valid session ID, send a POST request to '/api/{version}/objects/users/me/api_access_tokensys' (passing the session ID in the Authorization header) to create a permanent API access token. This token value is displayed only once upon creation and must be stored securely. It can be used in the 'Authorization' header of subsequent requests, prefixed with 'Bearer ' (e.g., 'Authorization: Bearer <token_value>').

2. Add them to .dlt/secrets.toml

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[sources.veeva_vault_source]
api_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 Veeva Vault data can I load into DuckDB?

These are the Veeva Vault endpoints dlt can load into DuckDB:

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How do I load only new Veeva Vault records?

Veeva Vault exposes modified_datev on api/{version}/objects/{object_name}, 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": "objects", "endpoint": {
    "path": "api/{version}/objects/{object_name}",
    "data_selector": "data",
    "incremental": {"cursor_path": "modified_datev", "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 Veeva Vault pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading '/api/{version}/auth' and '/api/{version}/query' from the Veeva Vault API into DuckDB:

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

@dlt.source
def veeva_vault_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{vaultDNS}/api/{version}",
            "auth": {"type": "bearer", "token": api_token},
        },
        "resources": [
            {"name": "objects", "endpoint": {"path": "api/{version}/objects/{object_name}", "data_selector": "data"}},
            {"name": "vql_query", "endpoint": {"path": "api/{version}/query", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_veeva_vault_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="veeva_vault_pipeline",
        destination="duckdb",
        dataset_name="veeva_vault_data",
    )
    load_info = pipeline.run(veeva_vault_source())
    print(load_info)


if __name__ == "__main__":
    load_veeva_vault_to_duckdb()

Run it with python veeva_vault_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 Veeva Vault 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("veeva_vault_pipeline").dataset()
df = data.vql_query.df()
print(df.head())

SQL:

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SELECT * FROM veeva_vault_data.vql_query LIMIT 10;

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

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How do I deploy the Veeva Vault 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 Veeva Vault 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 Veeva Vault 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