Load Atum REST API data to DuckDB
Build a Atum REST API to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Atum REST API API base URL, auth, endpoints, and incremental loading.
Atum is a WooCommerce and WordPress plugin that provides inventory and stock management endpoints via the WooCommerce REST API infrastructure. Everything needed to build a working Atum REST API → 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 Atum REST API to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Atum REST API 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 Atum REST API 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.
Atum REST API API at a glance
| Base URL | https://{your_store_domain}/wp-json/wc/v3 |
| Example endpoint | GET atum/purchase-orders |
| Authentication | all requests require HTTP Basic Authentication using WooCommerce consumer keys |
| Pagination | Page-number page size via per_page |
| Incremental field | page |
| Record id | id |
| API reference | https://stockmanagementlabs.github.io/atum-rest-api-docs/ |
These values come from the Atum REST API API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Atum REST API API?
Authentication is performed over HTTPS using HTTP Basic Authentication, where the WooCommerce REST API Consumer Key serves as the username and the Consumer Secret as the password. If the server strips the Authorization header, the credentials can be passed as query string parameters (consumer_key and consumer_secret).
1. Get your credentials
In your WordPress admin dashboard, navigate to WooCommerce > Settings > Advanced > REST API. Click Add key to create a new credential pair. Provide a description, select the user, and assign read or read/write permissions. Save the settings to generate your Consumer Key and Consumer Secret. Use the Consumer Key as the username and the Consumer Secret as the password for HTTP Basic Authentication.
2. Add them to .dlt/secrets.toml
[sources.atum_rest_api_source] consumer_key = "ck_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" consumer_secret = "cs_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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 Atum REST API data can I load into DuckDB?
These are the Atum REST API endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| purchase_orders | atum/purchase-orders | GET | List all purchase orders | |
| purchase_orders_detail | atum/purchase-orders/{id} | GET | Retrieve a single purchase order by ID | |
| inventories | atum/inventories | GET | List all inventories | |
| inbound_stock | atum/inbound-stock | GET | List all inbound stock records | |
| suppliers | atum/suppliers | GET | List suppliers | |
| tools | atum/tools | GET | List tools |
How do I load only new Atum REST API records?
Atum REST API exposes page on atum/purchase-orders, 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": "purchase_orders", "endpoint": { "path": "atum/purchase-orders", "incremental": {"cursor_path": "page", "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 Atum REST API pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading products and orders from the Atum REST API API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def atum_rest_api_source(consumer_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://{your_store_domain}/wp-json/wc/v3", "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": consumer_key}, }, "resources": [ {"name": "purchase_orders", "endpoint": {"path": "atum/purchase-orders"}}, {"name": "inventories", "endpoint": {"path": "atum/inventories"}} ], } yield from rest_api_resources(config) def load_atum_rest_api_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="atum_rest_api_pipeline", destination="duckdb", dataset_name="atum_rest_api_data", ) load_info = pipeline.run(atum_rest_api_source()) print(load_info) if __name__ == "__main__": load_atum_rest_api_to_duckdb()
Run it with python atum_rest_api_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 Atum REST API 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("atum_rest_api_pipeline").dataset() df = data.purchase_orders.df() print(df.head())
SQL:
SELECT * FROM atum_rest_api_data.purchase_orders LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Atum REST API 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 Atum REST API 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 Atum REST API 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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