TIBCO(R) Data Virtualization Python API Docs | dltHub

TIBCO Data Virtualization is a data integration platform that allows users to publish and access data as RESTful web services. The REST API base URL is http://localhost:9400 or https://localhost:9402 and requests require an Authorization header with a Bearer token.

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 TIBCO(R) Data Virtualization data in under 10 minutes.

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What data can I load from TIBCO(R) Data Virtualization?

Here are some of the endpoints you can load from TIBCO(R) Data Virtualization:

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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 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 TIBCO(R) Data Virtualization 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 tibco_r_data_virtualization_pipeline.py

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

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Pipeline tibco_r_data_virtualization_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset tibco_r_data_virtualization_data
The duckdb destination used duckdb:/tibco_r_data_virtualization.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 tibco_r_data_virtualization_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 rest/v2/configs and rest/cbs/v1/assignments from the TIBCO(R) Data Virtualization 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 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 get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tibco_r_data_virtualization_pipeline",
        destination="duckdb",
        dataset_name="tibco_r_data_virtualization_data",
    )
    load_info = pipeline.run(tibco_r_data_virtualization_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("tibco_r_data_virtualization_pipeline").dataset()
sessions_df = data.resources.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM tibco_r_data_virtualization_data.resources LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("tibco_r_data_virtualization_pipeline").dataset()
data.resources.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 TIBCO(R) Data Virtualization 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