Load Kyligence data to Microsoft Fabric

Kyligence provides a data analytics platform that offers a REST API for managing queries, cubes, and system resources. Everything needed to build a working Kyligence → 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 Kyligence 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 Kyligence 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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Kyligence API at a glance

These values come from the Kyligence API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kyligence API?

Authentication uses Basic Authentication, where the 'Authorization' header must contain 'Basic ' followed by the base64-encoded 'username

' string.

1. Get your credentials

Log in to the Kyligence platform (e.g., Kyligence Zen) using your administrative account. Navigate to the user profile or security settings dashboard. Locate the API/Integration section where you can manage API keys. Select the option to create a new API key, provide a name for identification, and ensure you securely store the generated key, as it may not be visible again after creation. Manage, revoke, or rotate these keys as needed within this same dashboard interface to maintain security compliance.

2. Add them to .dlt/secrets.toml

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[sources.kyligence_source]
api_key = "REPLACE_ME"

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 Kyligence data can I load into Microsoft Fabric?

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

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How do I load only new Kyligence records?

The Kyligence 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": "cubes", "endpoint": {
    "path": "kylin/api/cubes",
    # 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 Kyligence pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading kylin/api/query and kylin/api/jobs from the Kyligence API into Microsoft Fabric:

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

@dlt.source
def kyligence_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<host>:<port>/kylin/api",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "cubes", "endpoint": {"path": "kylin/api/cubes", "data_selector": "cubes"}},
            {"name": "jobs", "endpoint": {"path": "kylin/api/jobs"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kyligence_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kyligence_pipeline",
        destination="fabric",
        dataset_name="kyligence_data",
    )
    load_info = pipeline.run(kyligence_source())
    print(load_info)


if __name__ == "__main__":
    load_kyligence_to_fabric()

Run it with python kyligence_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 Kyligence 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("kyligence_pipeline").dataset()
df = data.cubes.df()
print(df.head())

SQL:

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SELECT * FROM kyligence_data.cubes LIMIT 10;

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

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How do I deploy the Kyligence 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 Kyligence 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 Kyligence 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