Vena Python API Docs | dltHub

Vena Public API is a platform that allows users to extract data from data models, import data, and query ETL templates. The REST API base URL is https://{hub}.vena.io/api/public/v1 and All requests require HTTP Basic authentication using an API user and API key..

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 Vena data in under 10 minutes.

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What data can I load from Vena?

Here are some of the endpoints you can load from Vena:

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How do I authenticate with the Vena API?

The API uses HTTP Basic Authentication. The username is the 'apiUser' and the password is the 'apiKey' obtained from a generated application token in Vena.

1. Get your credentials

To obtain your Vena API credentials, log in to the Vena web application and navigate to the Admin tab. Within the admin interface, select the Application Tokens section. From here, you can generate a new application token or retrieve existing credentials. Ensure the token is assigned the appropriate Application Permissions for the operations you intend to perform. You will retrieve an 'apiUser' (username) and an 'apiKey' (password). If you do not see the Admin tab, contact your system administrator to have them provision these credentials for you.

2. Add them to .dlt/secrets.toml

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[sources.vena_source]
api_user = "your_api_user_id_here"
api_key = "your_api_key_here"
hub = "us1" # Example region, change based on your Vena URL (e.g., us1, us2, ca3)

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 harness:

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dlthub 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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dlthub 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 Vena 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 vena_pipeline.py

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

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Pipeline vena_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset vena_data
The duckdb destination used duckdb:/vena.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 vena_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 models and import from the Vena 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 vena_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{hub}.vena.io/api/public/v1",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "intersections", "endpoint": {"path": "models/{modelId}/intersections"}},
            {"name": "hierarchy", "endpoint": {"path": "models/{modelId}/hierarchy"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vena_pipeline",
        destination="duckdb",
        dataset_name="vena_data",
    )
    load_info = pipeline.run(vena_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("vena_pipeline").dataset()
sessions_df = data.intersections.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM vena_data.intersections LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("vena_pipeline").dataset()
data.intersections.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 Vena 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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Next steps

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

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

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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