Load BI Connector data to Microsoft Fabric

BI Connector is an integration tool that enables connectivity between BI platforms and Oracle Fusion Cloud applications, Oracle Analytics, and OBIEE data sources. Everything needed to build a working BI Connector → 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 BI Connector 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 BI Connector 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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BI Connector API at a glance

These values come from the BI Connector API reference — the authoritative source if anything here looks out of date.

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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 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 BI Connector data can I load into Microsoft Fabric?

These are the BI Connector endpoints dlt can load into Microsoft Fabric:

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How do I load only new BI Connector records?

BI Connector exposes offset on rest/v1/published-reports/{id}, 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": "published_report_details", "endpoint": {
    "path": "rest/v1/published-reports/{id}",
    "data_selector": "data",
    "incremental": {"cursor_path": "offset", "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 BI Connector pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /me and /data from the BI Connector API into Microsoft Fabric:

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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 load_bi_connector_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bi_connector_pipeline",
        destination="fabric",
        dataset_name="bi_connector_data",
    )
    load_info = pipeline.run(bi_connector_source())
    print(load_info)


if __name__ == "__main__":
    load_bi_connector_to_fabric()

Run it with python bi_connector_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 BI Connector 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("bi_connector_pipeline").dataset()
df = data.published_report_details.df()
print(df.head())

SQL:

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SELECT * FROM bi_connector_data.published_report_details LIMIT 10;

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

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How do I deploy the BI Connector 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 BI Connector 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 BI Connector 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