Load Power BI data to DuckDB

Power BI REST API provides programmatic access to manage, monitor, and automate Power BI service resources such as workspaces, datasets, and reports. Everything needed to build a working Power BI → 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 Power BI 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 Power BI 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 Power BI 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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Power BI API at a glance

These values come from the Power BI API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Power BI API?

All requests require an 'Authorization' header with a Bearer token. The token is obtained via OAuth 2.0 from Microsoft Entra ID (formerly Azure AD).

1. Get your credentials

1. Sign in to the Azure Portal and navigate to Microsoft Entra ID. 2. Register a new application under 'App registrations'. 3. Note the Application (client) ID and Directory (tenant) ID. 4. Create a client secret under 'Certificates & secrets' (or upload a certificate). 5. In the Power BI Admin Portal, go to 'Tenant settings' > 'Developer settings' and enable 'Allow service principals to use Power BI APIs'. Add your service principal to the designated security group. 6. Add the service principal as a member or admin to the required Power BI workspaces.

2. Add them to .dlt/secrets.toml

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[sources.power_bi_source]
access_token = "your_access_token_here"
# Alternatively, if using a connector that supports direct auth:
# client_id = "your_client_id"
# client_secret = "your_client_secret"
# tenant_id = "your_tenant_id"

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 Power BI data can I load into DuckDB?

These are the Power BI endpoints dlt can load into DuckDB:

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How do I load only new Power BI records?

The Power BI API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "reports", "endpoint": {
    "path": "v1.0/myorg/reports",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Power BI pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading 'datasets and reports' from the Power BI API into DuckDB:

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

@dlt.source
def power_bi_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.powerbi.com/v1.0/myorg",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "reports", "endpoint": {"path": "v1.0/myorg/reports", "data_selector": "value"}},
            {"name": "datasets", "endpoint": {"path": "v1.0/myorg/datasets", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_power_bi_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="power_bi_pipeline",
        destination="duckdb",
        dataset_name="power_bi_data",
    )
    load_info = pipeline.run(power_bi_source())
    print(load_info)


if __name__ == "__main__":
    load_power_bi_to_duckdb()

Run it with python power_bi_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 Power BI 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("power_bi_pipeline").dataset()
df = data.reports.df()
print(df.head())

SQL:

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SELECT * FROM power_bi_data.reports LIMIT 10;

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

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How do I deploy the Power BI 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 Power BI 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 Power BI 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