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Load Chargebee data to DuckDB

Build a Chargebee to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Chargebee API base URL, auth, endpoints, and incremental loading.

SourceChargebeeDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Chargebee is a subscription billing platform that provides a REST API for managing subscriptions, customers, invoices, and payments. Everything needed to build a working Chargebee → DuckDB 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.


Build your Chargebee to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from Chargebee to DuckDB and run it on dltHub

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 Chargebee 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.


Chargebee API at a glance

Base URLhttps://{site}.chargebee.com/api/v2
Example endpointGET customers
Records found atlist
Authenticationall requests require HTTP Basic authentication using an API key — sent in the request header
PaginationOffset-based via offset, next cursor at next_offset, page size via limit (default 10, max 100). The limit parameter specifies the page size. Pagination occurs via an offset-based mechanism where the next_offset value returned in the response should be passed as the offset parameter for the subsequent request. When offset is not provided, the first page is returned.
Incremental fieldupdated_at
Record idid
API referencehttps://apidocs.chargebee.com/docs/api/auth

These values come from the Chargebee API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Chargebee API?

Chargebee uses HTTP Basic authentication. You must provide your API key as the username and leave the password empty, typically handled via the Authorization header using the base64-encoded string 'api_key:'.

1. Get your credentials

To obtain your Chargebee API credentials, log in to your Chargebee application and navigate to Settings > Configure Chargebee > API Keys and Webhooks. Click on the API Keys tab, select Add an API Key, choose Full-Access Key (recommended for integration), provide a name, and save the key securely. Ensure you use the appropriate test or live site API key for your environment.

2. Add them to .dlt/secrets.toml

[sources.chargebee_source] api_key = "your_api_key_here" site_name = "your_site_name_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.


What Chargebee data can I load into DuckDB?

These are the Chargebee endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
customerscustomersGETlistList all customers
subscriptionssubscriptionsGETlistList all subscriptions
invoicesinvoicesGETlistList all invoices
transactionstransactionsGETlistList all transactions
credit_notescredit_notesGETlistList all credit notes

How do I load only new Chargebee records?

Chargebee exposes updated_at on customers, 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.

{"name": "customers", "endpoint": { "path": "customers", "data_selector": "list", "incremental": {"cursor_path": "updated_at", "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.


What does the generated Chargebee pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading customers and subscriptions from the Chargebee API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def chargebee_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://{site}.chargebee.com/api/v2", "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key}, }, "resources": [ {"name": "customers", "endpoint": {"path": "customers", "data_selector": "list"}}, {"name": "subscriptions", "endpoint": {"path": "subscriptions", "data_selector": "list"}} ], } yield from rest_api_resources(config) def load_chargebee_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="chargebee_pipeline", destination="duckdb", dataset_name="chargebee_data", ) load_info = pipeline.run(chargebee_source()) print(load_info) if __name__ == "__main__": load_chargebee_to_duckdb()

Run it with python chargebee_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query Chargebee data in DuckDB?

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):

import dlt data = dlt.pipeline("chargebee_pipeline").dataset() df = data.customers.df() print(df.head())

SQL:

SELECT * FROM chargebee_data.customers LIMIT 10;

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


How do I deploy the Chargebee to DuckDB 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 Chargebee loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load Chargebee data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

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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