Load Vena data to Microsoft Fabric

Vena Public API is a platform that allows users to extract data from data models, import data, and query ETL templates. Everything needed to build a working Vena → Microsoft Fabric 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.

---

Build your Vena to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

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 Vena 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.

---

Vena API at a glance

These values come from the Vena API reference — the authoritative source if anything here looks out of date.

---

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

Copy
[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 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.

---

What Vena data can I load into Microsoft Fabric?

These are the Vena endpoints dlt can load into Microsoft Fabric:

---

How do I load only new Vena records?

The Vena 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.

Copy
{"name": "intersections", "endpoint": {
    "path": "models/{modelId}/intersections",
    # 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.

---

What does the generated Vena pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading models and import from the Vena API into Microsoft Fabric:

Copy
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 load_vena_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vena_pipeline",
        destination="fabric",
        dataset_name="vena_data",
    )
    load_info = pipeline.run(vena_source())
    print(load_info)


if __name__ == "__main__":
    load_vena_to_fabric()

Run it with python vena_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Vena data in Microsoft Fabric?

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):

Copy
import dlt

data = dlt.pipeline("vena_pipeline").dataset()
df = data.intersections.df()
print(df.head())

SQL:

Copy
SELECT * FROM vena_data.intersections LIMIT 10;

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

---

How do I deploy the Vena to Microsoft Fabric 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 Vena loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Vena 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.

---

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