Load Gocardless data to DuckDB
Build a Gocardless to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Gocardless API base URL, auth, endpoints, and incremental loading.
GoCardless is a payment processing platform for collecting recurring and one-off payments from customers. Everything needed to build a working Gocardless → 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 Gocardless to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Gocardless 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 Gocardless 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.
Gocardless API at a glance
| Base URL | https://api.gocardless.com/ |
| Example endpoint | GET payments |
| Records found at | payments |
| Authentication | all requests require a Bearer token in the Authorization header — sent in the Authorization header, prefixed Bearer |
| Pagination | Cursor-based via after, before, page size via limit (default 50, max 500). The API uses cursor-based pagination. 'after' points to the ID of the resource after which to retrieve results, and 'before' points to the ID of the resource before which to retrieve results. Cursors are returned in the response under meta.cursors. |
| Incremental field | after |
| Record id | id |
| API reference | https://developer.gocardless.com/api-reference |
These values come from the Gocardless API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Gocardless API?
Authentication is performed by including an access token in the Authorization request header using the Bearer scheme. Additionally, the GoCardless-Version header is required for all requests (e.g., '2015-07-06').
1. Get your credentials
- Log in to your GoCardless Dashboard (or Sandbox Dashboard for testing). 2. Navigate to the Developer section. 3. Click the Create button in the top-right corner. 4. Select Access token. 5. Provide a name, choose the appropriate access scope (e.g., Read-Write), and click Create access token. 6. Copy the generated token immediately, as it cannot be viewed again once the window is closed.
2. Add them to .dlt/secrets.toml
[sources.gocardless_source] api_key = "your_access_token_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 Gocardless data can I load into DuckDB?
These are the Gocardless endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| customers | customers | GET | customers | Retrieve a list of customers. |
| payments | payments | GET | payments | Retrieve a list of payments. |
| mandates | mandates | GET | mandates | Retrieve a list of mandates. |
| subscriptions | subscriptions | GET | subscriptions | Retrieve a list of subscriptions. |
| payouts | payouts | GET | payouts | Retrieve a list of payouts. |
How do I load only new Gocardless records?
Gocardless exposes after on payments, 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": "payments", "endpoint": { "path": "payments", "data_selector": "payments", "incremental": {"cursor_path": "after", "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 Gocardless pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading billing_requests and billing_request_flows from the Gocardless API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def gocardless_source(access_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.gocardless.com/", "auth": {"type": "bearer", "token": access_token}, }, "resources": [ {"name": "payments", "endpoint": {"path": "payments", "data_selector": "payments"}}, {"name": "customers", "endpoint": {"path": "customers", "data_selector": "customers"}} ], } yield from rest_api_resources(config) def load_gocardless_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="gocardless_pipeline", destination="duckdb", dataset_name="gocardless_data", ) load_info = pipeline.run(gocardless_source()) print(load_info) if __name__ == "__main__": load_gocardless_to_duckdb()
Run it with python gocardless_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 Gocardless 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("gocardless_pipeline").dataset() df = data.payments.df() print(df.head())
SQL:
SELECT * FROM gocardless_data.payments LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Gocardless 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 Gocardless loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load Gocardless data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example 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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