Load TIBCO(R) Data Virtualization data to Microsoft Fabric

TIBCO Data Virtualization is a data integration platform that allows users to publish and access data as RESTful web services. Everything needed to build a working TIBCO(R) Data Virtualization → 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.

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Build your TIBCO(R) Data Virtualization 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 TIBCO(R) Data Virtualization 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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TIBCO(R) Data Virtualization API at a glance

These values come from the TIBCO(R) Data Virtualization API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the TIBCO(R) Data Virtualization API?

TIBCO Data Virtualization supports Bearer token authentication via the Authorization header, formatted as 'Bearer '.

1. Get your credentials

TIBCO Data Virtualization (TDV) does not use a central API key management dashboard for its REST API. Instead, authentication is managed on a per-service or per-session basis using standard HTTP methods. To authenticate, you should either: 1) Use Basic Authentication by providing a valid TDV username and password in the request header. 2) Use OAuth2 by configuring the service for Bearer token authentication, which requires obtaining an access token from your configured OAuth2 provider. 3) Use Session Tokens by calling the authentication service to retrieve a session ID, which can then be passed in subsequent requests via headers or cookies. Access to specific REST services is governed by the READ privileges assigned to your user account within TDV Studio.

2. Add them to .dlt/secrets.toml

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[sources.tibco_r_data_virtualization_source]
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 TIBCO(R) Data Virtualization data can I load into Microsoft Fabric?

These are the TIBCO(R) Data Virtualization endpoints dlt can load into Microsoft Fabric:

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How do I load only new TIBCO(R) Data Virtualization records?

TIBCO(R) Data Virtualization exposes skiptoken on odata4/webservices/resources, 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": "resources", "endpoint": {
    "path": "odata4/webservices/resources",
    "data_selector": "value",
    "incremental": {"cursor_path": "skiptoken", "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 TIBCO(R) Data Virtualization pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading rest/v2/configs and rest/cbs/v1/assignments from the TIBCO(R) Data Virtualization API into Microsoft Fabric:

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

@dlt.source
def tibco_r_data_virtualization_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:9400 or https://localhost:9402",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "resources", "endpoint": {"path": "odata4/webservices/resources", "data_selector": "value"}},
            {"name": "metadata", "endpoint": {"path": "odata4/webservices/$metadata", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_tibco_r_data_virtualization_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tibco_r_data_virtualization_pipeline",
        destination="fabric",
        dataset_name="tibco_r_data_virtualization_data",
    )
    load_info = pipeline.run(tibco_r_data_virtualization_source())
    print(load_info)


if __name__ == "__main__":
    load_tibco_r_data_virtualization_to_fabric()

Run it with python tibco_r_data_virtualization_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 TIBCO(R) Data Virtualization 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):

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import dlt

data = dlt.pipeline("tibco_r_data_virtualization_pipeline").dataset()
df = data.resources.df()
print(df.head())

SQL:

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SELECT * FROM tibco_r_data_virtualization_data.resources LIMIT 10;

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

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How do I deploy the TIBCO(R) Data Virtualization 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 TIBCO(R) Data Virtualization 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 TIBCO(R) Data Virtualization 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