BI Connector Python API Docs | dltHub

BI Connector is an integration tool that enables connectivity between BI platforms and Oracle Fusion Cloud applications, Oracle Analytics, and OBIEE data sources. The REST API base URL is https://<your-instance>.oraclecloud.com and all requests require a Bearer token via OAuth 2.0 or JWT authentication.

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 BI Connector data in under 10 minutes.

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What data can I load from BI Connector?

Here are some of the endpoints you can load from BI Connector:

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How do I authenticate with the BI Connector API?

Authentication is primarily handled via OAuth 2.0 or JWT (JSON Web Token), typically requiring an 'Authorization' header with the value 'Bearer <access_token>'. For JWT setups, various parameters such as Client ID, Client Secret, Scope, Key Alias, and Private Key are required to generate the token internally.

1. Get your credentials

1. Access your BI Connector web application URL provided by your Gateway Administrator. 2. Log in with your assigned credentials. 3. Navigate to the API Key section in the left-hand sidebar. 4. Copy the displayed API key to a secure location, as you will need it for your dlt pipeline configuration.

2. Add them to .dlt/secrets.toml

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[sources.bi_connector_source]
api_key = "your_api_key_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 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 BI Connector 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 bi_connector_pipeline.py

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

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Pipeline bi_connector_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bi_connector_data
The duckdb destination used duckdb:/bi_connector.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 bi_connector_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 /me and /data from the BI Connector 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 bi_connector_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-instance>.oraclecloud.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "published_report_details", "endpoint": {"path": "rest/v1/published-reports/{id}", "data_selector": "data"}},
            {"name": "published_reports", "endpoint": {"path": "rest/v1/published-reports"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bi_connector_pipeline",
        destination="duckdb",
        dataset_name="bi_connector_data",
    )
    load_info = pipeline.run(bi_connector_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("bi_connector_pipeline").dataset()
sessions_df = data.published_report_details.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bi_connector_data.published_report_details LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bi_connector_pipeline").dataset()
data.published_report_details.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 BI Connector 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