FastBill Python API Docs | dltHub

FastBill is an XML/JSON Webservice where all resources are accessed via a central URL, with all API requests sent as POST. The REST API base URL is https://my.fastbill.com/api/1.0/api.php and All requests require HTTP Basic Authentication using the FastBill account email as username and API-Key as 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 FastBill data in under 10 minutes.

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What data can I load from FastBill?

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

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How do I authenticate with the FastBill API?

Authentication is performed using HTTP Basic Auth, where the FastBill account email is the username and the API-Key is the password. For Add-on authentication, X-Username and X-Password headers must also be supplied, in addition to the basic auth with Add-on credentials.

1. Get your credentials

Instructions for obtaining API credentials from the provider's dashboard are not explicitly detailed in the provided documentation. The authentication uses an existing FastBill User (E-Mail Address) and the API-Key of the respective FastBill Account.

2. Add them to .dlt/secrets.toml

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[sources.fastbill_source]
email = "your_fastbill_email_here"
api_key = "your_fastbill_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 FastBill 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 fastbill_pipeline.py

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

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Pipeline fastbill_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fastbill_data
The duckdb destination used duckdb:/fastbill.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 fastbill_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 invoices from the FastBill 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 fastbill_source(email, api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://my.fastbill.com/api/1.0/api.php",
            "auth": {
                "type": "http_basic",
                "api_key": email, api_key,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "api.php", "data_selector": "RESPONSE.CUSTOMERS"}},
            {"name": "invoices", "endpoint": {"path": "api.php", "data_selector": "RESPONSE.INVOICES"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM fastbill_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fastbill_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 FastBill 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

If authentication fails, it is likely due to invalid credentials. Ensure that the correct FastBill account email is used as the username and the API-Key as the password for HTTP Basic Authentication.

Rate Limits

The FastBill API imposes rate limits based on the subscription plan:

• Solo: 50 Calls/h
• Solo-Plus: 100 Calls/h
• Pro: 500 Calls/h
• Premium: 1000 Calls/h Exceeding these limits will result in errors. Monitor your API usage and consider upgrading your plan if higher limits are needed.

Pagination

When retrieving lists of resources, the default LIMIT is 10, with a maximum of 100 elements per retrieval. To access more records, use the LIMIT and OFFSET parameters in the request body to paginate through the results.

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