Load Azure REST API data to DuckDB

Azure REST API provides a programmatic interface for managing Azure services and resources across the Microsoft cloud platform. Everything needed to build a working Azure REST API → 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 Azure REST API 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 Azure REST API 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 Azure REST API 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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Azure REST API API at a glance

These values come from the Azure REST API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Azure REST API API?

Azure REST APIs generally require an Authorization header using a Bearer token, which is obtained via Microsoft Entra ID (OAuth 2.0). The header format is 'Authorization: Bearer '.

1. Get your credentials

To obtain credentials for the Azure REST API, you must register a Service Principal (an application identity) in Microsoft Entra ID (formerly Azure AD) via the Azure Portal. 1. In the Azure Portal, navigate to App registrations and create a new registration. 2. Once registered, generate a Client Secret under the "Certificates & secrets" section. 3. Assign the necessary Azure RBAC roles (e.g., Reader, Contributor) to this Service Principal at the desired scope (subscription or resource group level). 4. Collect the Application (client) ID, Directory (tenant) ID, and the Client Secret value. Use these credentials to authenticate via OAuth 2.0 to obtain an access token (Bearer token) from the Microsoft identity platform /token endpoint for the resource https://management.azure.com/.

2. Add them to .dlt/secrets.toml

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[sources.azure_rest_api_source]
management_token = "your_bearer_token_here" # If using a custom authentication flow requiring client credentials:
# 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 Azure REST API data can I load into DuckDB?

These are the Azure REST API endpoints dlt can load into DuckDB:

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How do I load only new Azure REST API records?

Azure REST API exposes nextLink on subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/resources, 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": "resources", "endpoint": {
    "path": "subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/resources",
    "data_selector": "value",
    "incremental": {"cursor_path": "nextLink", "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 Azure REST API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading subscriptions and resourceGroups from the Azure REST API API into DuckDB:

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

@dlt.source
def azure_rest_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://management.azure.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "resources", "endpoint": {"path": "subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/resources", "data_selector": "value"}},
            {"name": "resource_graph", "endpoint": {"path": "providers/Microsoft.ResourceGraph/resources?api-version=2021-03-01", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_azure_rest_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="azure_rest_api_pipeline",
        destination="duckdb",
        dataset_name="azure_rest_api_data",
    )
    load_info = pipeline.run(azure_rest_api_source())
    print(load_info)


if __name__ == "__main__":
    load_azure_rest_api_to_duckdb()

Run it with python azure_rest_api_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 Azure REST API 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("azure_rest_api_pipeline").dataset()
df = data.resources.df()
print(df.head())

SQL:

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SELECT * FROM azure_rest_api_data.resources LIMIT 10;

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

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How do I deploy the Azure REST API 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 Azure REST API 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 Azure REST API 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