Load Wolters Kluwer CCH Tagetik data to Microsoft Fabric

Wolters Kluwer CCH Tagetik is a Corporate Performance Management platform that exposes financial and analytical data via OData v4 REST APIs. Everything needed to build a working Wolters Kluwer CCH Tagetik → 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 Wolters Kluwer CCH Tagetik 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 Wolters Kluwer CCH Tagetik 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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Wolters Kluwer CCH Tagetik API at a glance

These values come from the Wolters Kluwer CCH Tagetik API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Wolters Kluwer CCH Tagetik API?

The API supports Basic Authentication (using username/password) and OAuth 2.0 Client Credentials flow. Requests typically require an 'Authorization' header ('Basic <base64_encoded_creds>' or 'Bearer '), along with 'OData-Version' and 'Accept' headers.

1. Get your credentials

To obtain API credentials for the CCH Tagetik REST API, you must log in to your CCH Tagetik administrative dashboard. Navigate to the system settings or integration management section where ad-hoc API credentials can be configured. Depending on your security requirements, you can set up either Basic Authentication (using a username and password) or OAuth 2.0 Client Credentials. For OAuth 2.0, you will be provided with a Client ID and a Client Secret. Consult the CCH Tagetik help documentation at https://help.tagetik.com for instance-specific path instructions as administrative layouts may vary by environment.

2. Add them to .dlt/secrets.toml

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[sources.wolters_kluwer_cch_tagetik_source]
tagetik_client_id = "your_client_id_here"
tagetik_client_secret = "your_client_secret_here"
tagetik_environment_url = "https://your-instance.tagetik.com"

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 Wolters Kluwer CCH Tagetik data can I load into Microsoft Fabric?

These are the Wolters Kluwer CCH Tagetik endpoints dlt can load into Microsoft Fabric:

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How do I load only new Wolters Kluwer CCH Tagetik records?

Wolters Kluwer CCH Tagetik exposes $skip on odata/{database}/{entity}, 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": "financial_data", "endpoint": {
    "path": "odata/{database}/{entity}",
    "data_selector": "value",
    "incremental": {"cursor_path": "$skip", "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 Wolters Kluwer CCH Tagetik pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading $metadata and odata from the Wolters Kluwer CCH Tagetik API into Microsoft Fabric:

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

@dlt.source
def wolters_kluwer_cch_tagetik_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{your-tagetik-environment}/",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "financial_data", "endpoint": {"path": "odata/{database}/{entity}", "data_selector": "value"}},
            {"name": "scim_users", "endpoint": {"path": "scim/v2/Users", "data_selector": "Resources"}}
        ],
    }
    yield from rest_api_resources(config)


def load_wolters_kluwer_cch_tagetik_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wolters_kluwer_cch_tagetik_pipeline",
        destination="fabric",
        dataset_name="wolters_kluwer_cch_tagetik_data",
    )
    load_info = pipeline.run(wolters_kluwer_cch_tagetik_source())
    print(load_info)


if __name__ == "__main__":
    load_wolters_kluwer_cch_tagetik_to_fabric()

Run it with python wolters_kluwer_cch_tagetik_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 Wolters Kluwer CCH Tagetik 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("wolters_kluwer_cch_tagetik_pipeline").dataset()
df = data.financial_data.df()
print(df.head())

SQL:

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SELECT * FROM wolters_kluwer_cch_tagetik_data.financial_data LIMIT 10;

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

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How do I deploy the Wolters Kluwer CCH Tagetik 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 Wolters Kluwer CCH Tagetik 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 Wolters Kluwer CCH Tagetik 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