Load Microsoft Intune data to DuckDB

Microsoft Intune API allows enterprises to manage devices, apps, and configuration within an organization through Microsoft Graph. Everything needed to build a working Microsoft Intune → 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 Intune 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 Intune 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 Intune 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 Intune API at a glance

These values come from the Microsoft Intune API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Microsoft Intune API?

Authentication is handled via OAuth 2.0 using the Microsoft identity platform. Access tokens must be passed as a Bearer token in the 'Authorization' header of every request.

1. Get your credentials

1. Sign in to the Microsoft Entra admin center (https://entra.microsoft.com/) using an administrative account. 2. Navigate to Identity > Applications > App registrations. 3. Select New registration to create an application, or choose an existing one. 4. Once registered, navigate to API permissions and add the necessary Microsoft Graph application permissions (e.g., DeviceManagementManagedDevices.Read.All). 5. Click Grant admin consent for your tenant to finalize permissions. 6. Go to Certificates & secrets in the navigation menu, select New client secret, add a description/expiry, and click Add. 7. Copy the generated client secret value immediately, as it will not be displayed again. Save the Application (client) ID and Directory (tenant) ID from the app's Overview page.

2. Add them to .dlt/secrets.toml

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[sources.microsoft_intune_source]
tenant_id = "your_tenant_id_here"
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
scope = "https://graph.microsoft.com/.default"

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 Intune data can I load into DuckDB?

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

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How do I load only new Microsoft Intune records?

Microsoft Intune exposes @odata.nextLink on deviceManagement/managedDevices, 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": "managed_devices", "endpoint": {
    "path": "deviceManagement/managedDevices",
    "data_selector": "value",
    "incremental": {"cursor_path": "@odata.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 Microsoft Intune pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /deviceManagement/managedDevices and /deviceManagement/deviceConfigurations from the Microsoft Intune API into DuckDB:

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

@dlt.source
def microsoft_intune_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://graph.microsoft.com/v1.0",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "managed_devices", "endpoint": {"path": "deviceManagement/managedDevices", "data_selector": "value"}},
            {"name": "mobile_apps", "endpoint": {"path": "deviceManagement/mobileApps", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_microsoft_intune_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_intune_pipeline",
        destination="duckdb",
        dataset_name="microsoft_intune_data",
    )
    load_info = pipeline.run(microsoft_intune_source())
    print(load_info)


if __name__ == "__main__":
    load_microsoft_intune_to_duckdb()

Run it with python microsoft_intune_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 Intune 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_intune_pipeline").dataset()
df = data.managed_devices.df()
print(df.head())

SQL:

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SELECT * FROM microsoft_intune_data.managed_devices LIMIT 10;

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

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How do I deploy the Microsoft Intune 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 Intune 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 Intune 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