Load InformationGrid data to Microsoft Fabric

InGrid-API is a centralized interface for managing InGrid datasets and metadata search capabilities. Everything needed to build a working InformationGrid → 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 InformationGrid 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 InformationGrid 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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InformationGrid API at a glance

These values come from the InformationGrid API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the InformationGrid API?

Authentication is managed via Keycloak using OAuth2. Clients must include a bearer token in the Authorization header for API requests.

1. Get your credentials

To obtain your API credentials for the Ingrid (InformationGrid) platform, register an account on the official Ingrid developer portal. Upon registration, you will be issued an API token, which must be included in every request within the 'Authorization' HTTP header using the 'Bearer' prefix (e.g., 'Authorization: Bearer <your_token_here>'). Keep this token secure, as it functions like a password, and ensure backend-only usage.

2. Add them to .dlt/secrets.toml

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[sources.informationgrid_source]
api_token = "your_token_here"

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 InformationGrid data can I load into Microsoft Fabric?

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

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How do I load only new InformationGrid records?

The InformationGrid 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": "catalogs", "endpoint": {
    "path": "/portal/catalogs",
    # 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 InformationGrid pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading session.create and siw/session.create from the InformationGrid API into Microsoft Fabric:

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

@dlt.source
def informationgrid_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8550/v3/api-docs",
            "auth": {"type": "bearer", "token": token},
        },
        "resources": [
            {"name": "catalogs", "endpoint": {"path": "/portal/catalogs"}},
            {"name": "search", "endpoint": {"path": "/portal/search"}}
        ],
    }
    yield from rest_api_resources(config)


def load_informationgrid_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="informationgrid_pipeline",
        destination="fabric",
        dataset_name="informationgrid_data",
    )
    load_info = pipeline.run(informationgrid_source())
    print(load_info)


if __name__ == "__main__":
    load_informationgrid_to_fabric()

Run it with python informationgrid_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 InformationGrid 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("informationgrid_pipeline").dataset()
df = data.catalogs.df()
print(df.head())

SQL:

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SELECT * FROM informationgrid_data.catalogs LIMIT 10;

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

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How do I deploy the InformationGrid 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 InformationGrid 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 InformationGrid 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