Veeva Vault Python API Docs | dltHub

Veeva Vault REST API is a platform that provides access to various Vault functionalities, including retrieving API versions, managing objects, and checking job statuses. The REST API base URL is https://{vaultDNS}/api/{version} and Veeva Vault uses session-based authentication via a sessionId..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Veeva Vault data in under 10 minutes.

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What data can I load from Veeva Vault?

Here are some of the endpoints you can load from Veeva Vault:

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How do I authenticate with the Veeva Vault API?

Vault uses session-based authentication where a sessionId obtained from an authentication call is used in the Authorization header as Authorization: {SESSION_ID}. It supports username/password authentication and OAuth2/OIDC to obtain the sessionId.

1. Get your credentials

To obtain Vault user credentials, an administrator can provide them. Then, use a POST request to /api/{version}/auth with a username and password (Content-Type application/x-www-form-urlencoded) to receive a sessionId and a list of vaults. For OAuth2/OIDC, obtain an access_token from the identity provider and then POST to the login endpoint to exchange it for a sessionId.

2. Add them to .dlt/secrets.toml

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[sources.veeva_vault_source]
session_id = "your_session_id_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Veeva Vault API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python veeva_vault_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline veeva_vault_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset veeva_vault_data
The duckdb destination used duckdb:/veeva_vault.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline veeva_vault_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads vobjects and users from the Veeva Vault API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

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


def get_data() -> 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)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("veeva_vault_pipeline").dataset()
sessions_df = data.vobjects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM veeva_vault_data.vobjects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("veeva_vault_pipeline").dataset()
data.vobjects.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Veeva Vault data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

API Errors

The Veeva Vault API returns various error types with a responseStatus field. Common error types include INVALID_SESSION_ID, USERNAME_OR_PASSWORD_INCORRECT, API_LIMIT_EXCEEDED, INSUFFICIENT_ACCESS, MALFORMED_URL, METHOD_NOT_SUPPORTED, and UNEXPECTED_ERROR.

Rate Limits

The job status endpoint is limited to one call every 10 seconds; exceeding this limit will result in an API_LIMIT_EXCEEDED error. Authentication API calls are also rate-limited per username and domain, with throttling details provided in the response headers.

Pagination

Many endpoints support pagination, returning fields such as limit, offset, total, next_page, and previous_page. The data array key typically holds the records for vobjects or a specific plural key like users. Default page size is 200, which is also the maximum. Note that next_page and previous_page URLs expire after approximately 15 minutes. Both offset and limit parameters are supported for pagination.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack