Cheddar Python API Docs | dltHub

Cheddar is a recurring billing and subscription management platform exposing a REST‑like XML API. The REST API base URL is https://getcheddar.com/xml and All requests use HTTP Basic authentication (username + API key/password)..

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 Cheddar data in under 10 minutes.

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What data can I load from Cheddar?

Here are some of the endpoints you can load from Cheddar:

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How do I authenticate with the Cheddar API?

Authentication is performed with HTTP Basic Auth. Use the authorized user's email as the username and the account password or API key as the password in the Authorization header (e.g., curl -u "email:api_key").

1. Get your credentials

1. Log in to your Cheddar admin at https://getcheddar.com/admin.
2. Create or select a user account dedicated for API access (recommended).
3. Use that user's email as the username.
4. Use the user's account password or API key (where supported) as the password when making HTTP Basic‑authenticated requests.
5. If an API‑specific key is available in account settings or profile, copy it and use it as the password for API calls.

2. Add them to .dlt/secrets.toml

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[sources.cheddar_source]
api_key = "your_api_password_or_api_key_here"

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 Workbench:

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dlt 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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dlt 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 Cheddar 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 cheddar_pipeline.py

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

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Pipeline cheddar_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cheddar_data
The duckdb destination used duckdb:/cheddar.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 cheddar_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 customers and plans from the Cheddar 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 cheddar_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://getcheddar.com/xml",
            "auth": {
                "type": "http_basic",
                "password": api_key,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "customers/get/productCode/{productCode}", "data_selector": "customers"}},
            {"name": "plans", "endpoint": {"path": "plans/get/productCode/{productCode}", "data_selector": "plans"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cheddar_pipeline",
        destination="duckdb",
        dataset_name="cheddar_data",
    )
    load_info = pipeline.run(cheddar_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("cheddar_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cheddar_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cheddar_pipeline").dataset()
data.customers.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 Cheddar 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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Troubleshooting

Authentication failures

Cheddar uses HTTP Basic auth. Ensure you pass Authorization: Basic <base64(email:password_or_api_key)>. Using an incorrect email, password, or productCode will return HTTP 4xx errors and XML error payloads such as <error id="123" code="401">Authentication failed</error>.

XML response format and data selection

Responses are returned as XML documents (e.g., <customers>, <plans>, <invoices>). Parse XML and target the root collection element (e.g., <customers>) as the records container. There is no JSON response by default; convert XML to JSON before applying JSON selectors.

Errors, status codes and error payloads

Cheddar returns standard HTTP status codes. Errors include XML formatted error nodes such as: <error id="1234" code="404" auxCode="">Customer not found</error>. Check the error code attribute and HTTP status for handling.

Pagination / Large result sets

API methods for listing resources support filtering by parameters (productCode, id, code, invoiceNumber). If results are large, use product‑level filters and specific identifiers. If the API exposes offset/limit in request dictionary, include those; otherwise page by narrower filters.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime 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