Load Kognitwin data to Microsoft Fabric

Kognitwin is a cloud-native digital twin platform by Kongsberg Digital that provides industrial simulation, orchestration, and asset data management capabilities through RESTful interfaces. Everything needed to build a working Kognitwin → 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 Kognitwin 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 Kognitwin 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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Kognitwin API at a glance

These values come from the Kognitwin API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kognitwin API?

The Kognitwin API (specifically regarding Databricks integration) utilizes authentication via a configured source object in the system. While documentation for the specific header keys for generic REST interactions is limited, standard integration practice involves setting up a 'RemoteWebApi' source.

1. Get your credentials

Kognitwin (developed by Kongsberg Digital) API authentication typically uses a Bearer token obtained through OAuth 2.0 Client Credentials flow. 1. Identify your Client ID and Client Secret from your Kognitwin administrator dashboard or secret management service. 2. Send a POST request to your designated token endpoint with 'grant_type=client_credentials', 'client_id', and 'client_secret'. 3. Extract the 'access_token' from the JSON response to use in subsequent requests. Note: Ensure your integration requests include the 'Authorization: Bearer <access_token>' header.

2. Add them to .dlt/secrets.toml

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[sources.kognitwin_source]
kognitwin_client_id = "your_client_id_here"
kognitwin_client_secret = "your_client_secret_here"
kognitwin_token_url = "https://your-kognitwin-host/oauth/token"
kognitwin_api_base_url = "https://your-kognitwin-host/api"

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 Kognitwin data can I load into Microsoft Fabric?

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

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How do I load only new Kognitwin records?

The Kognitwin 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": "assets", "endpoint": {
    "path": "assets",
    # 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 Kognitwin pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading assets and messages from the Kognitwin API into Microsoft Fabric:

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

@dlt.source
def kognitwin_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kognitwin.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "assets"}},
            {"name": "telemetry", "endpoint": {"path": "telemetry"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kognitwin_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kognitwin_pipeline",
        destination="fabric",
        dataset_name="kognitwin_data",
    )
    load_info = pipeline.run(kognitwin_source())
    print(load_info)


if __name__ == "__main__":
    load_kognitwin_to_fabric()

Run it with python kognitwin_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 Kognitwin 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("kognitwin_pipeline").dataset()
df = data.assets.df()
print(df.head())

SQL:

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SELECT * FROM kognitwin_data.assets LIMIT 10;

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

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How do I deploy the Kognitwin 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 Kognitwin 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 Kognitwin 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