Wolters Kluwer CCH Tagetik Python API Docs | dltHub

Wolters Kluwer CCH Tagetik is a Corporate Performance Management platform that exposes financial and analytical data via OData v4 REST APIs. The REST API base URL is https://{your-tagetik-environment}/ and supports Basic Authentication and OAuth 2.0 Client Credentials flow.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Wolters Kluwer CCH Tagetik data in under 10 minutes.

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What data can I load from Wolters Kluwer CCH Tagetik?

Here are some of the endpoints you can load from Wolters Kluwer CCH Tagetik:

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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 automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

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dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlthub ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Wolters Kluwer CCH Tagetik API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python wolters_kluwer_cch_tagetik_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline wolters_kluwer_cch_tagetik_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wolters_kluwer_cch_tagetik_data
The duckdb destination used duckdb:/wolters_kluwer_cch_tagetik.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline wolters_kluwer_cch_tagetik_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads $metadata and odata from the Wolters Kluwer CCH Tagetik API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("wolters_kluwer_cch_tagetik_pipeline").dataset()
sessions_df = data.financial_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wolters_kluwer_cch_tagetik_data.financial_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wolters_kluwer_cch_tagetik_pipeline").dataset()
data.financial_data.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Wolters Kluwer CCH Tagetik data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack