Load Microsoft Fabric data to DuckDB

Microsoft Fabric REST API provides programmatic access to the unified analytics platform for managing workspaces, lakehouses, warehouses, data pipelines, and other Fabric items. Everything needed to build a working Microsoft Fabric → DuckDB 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 Microsoft Fabric to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Microsoft Fabric to DuckDB and run it on dltHub

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 Microsoft Fabric 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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Microsoft Fabric API at a glance

These values come from the Microsoft Fabric API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Microsoft Fabric API?

The API uses Microsoft Entra ID OAuth 2.0 access tokens. The token must be passed in the Authorization header as a Bearer token in the format 'Authorization: Bearer '.

1. Get your credentials

Microsoft Fabric REST APIs do not use static API keys; they require OAuth 2.0 bearer tokens acquired via Microsoft Entra ID. To obtain these credentials: 1. Sign in to the Microsoft Entra admin center. 2. Register a new application under App registrations and note the Application (client) ID. 3. Navigate to Certificates & secrets to create a new client secret. 4. A Fabric administrator must enable the Service principals can use Fabric APIs setting in the Fabric Admin portal. 5. Grant the application necessary permissions and assign it to the required workspaces (Viewer or Contributor roles). 6. To get an access token, send a POST request to https://login.microsoftonline.com/<TENANT_ID>/oauth2/v2.0/token with grant_type=client_credentials, your client_id, client_secret, and scope=https://api.fabric.microsoft.com/.default.

2. Add them to .dlt/secrets.toml

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[sources.microsoft_fabric_source]
access_token = "your_access_token_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 Microsoft Fabric data can I load into DuckDB?

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

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How do I load only new Microsoft Fabric records?

Microsoft Fabric exposes continuationToken on v1/workspaces, 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": "workspaces", "endpoint": {
    "path": "v1/workspaces",
    "data_selector": "value",
    "incremental": {"cursor_path": "continuationToken", "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 Microsoft Fabric pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading workspaces and admin/workspaces from the Microsoft Fabric API into DuckDB:

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

@dlt.source
def microsoft_fabric_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fabric.microsoft.com/v1/",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "workspaces", "endpoint": {"path": "v1/workspaces", "data_selector": "value"}},
            {"name": "items", "endpoint": {"path": "v1/workspaces/{workspaceId}/items", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_microsoft_fabric_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_fabric_pipeline",
        destination="duckdb",
        dataset_name="microsoft_fabric_data",
    )
    load_info = pipeline.run(microsoft_fabric_source())
    print(load_info)


if __name__ == "__main__":
    load_microsoft_fabric_to_duckdb()

Run it with python microsoft_fabric_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 Microsoft Fabric data in DuckDB?

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("microsoft_fabric_pipeline").dataset()
df = data.items.df()
print(df.head())

SQL:

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SELECT * FROM microsoft_fabric_data.items LIMIT 10;

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

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How do I deploy the Microsoft Fabric to DuckDB 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 Microsoft Fabric 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 Microsoft Fabric 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