Load Commerce Layer data to DuckDB

Commerce Layer is a headless e-commerce platform providing an API-first commerce engine for global brands. Everything needed to build a working Commerce Layer → 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.

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Build your Commerce Layer to DuckDB pipeline

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

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Commerce Layer 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 Commerce Layer 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.

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Commerce Layer API at a glance

These values come from the Commerce Layer API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Commerce Layer API?

All API requests require an Authorization header with a Bearer token; the token is obtained via a POST request to https://auth.commercelayer.io/oauth/token. Additionally, requests must specify 'Accept: application/vnd.api+json' and, for POST/PUT/PATCH, 'Content-Type: application/vnd.api+json'.

1. Get your credentials

To obtain API credentials for Commerce Layer: 1. Log in to your Commerce Layer admin dashboard. 2. In the left navigation sidebar, locate the Developers dropdown menu. 3. Click on API credentials. 4. Click the + New button in the top right corner. 5. Select the appropriate credential type (Integration, Sales channel, or Webapp) based on your requirements. 6. Provide a name, configure permissions/roles if prompted, and click Create. 7. Copy and save your Client ID, Client Secret, and organization slug (from your base endpoint), as they will be required for API authentication.

2. Add them to .dlt/secrets.toml

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[sources.commerce_layer_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
organization_slug = "your_org_slug_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.

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What Commerce Layer data can I load into DuckDB?

These are the Commerce Layer endpoints dlt can load into DuckDB:

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How do I load only new Commerce Layer records?

Commerce Layer exposes updated_at on api/skus, 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.

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{"name": "skus", "endpoint": {
    "path": "api/skus",
    "data_selector": "data",
    "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.

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What does the generated Commerce Layer pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading oauth/token (for authentication) and api/orders (as an example of a core resource endpoint) from the Commerce Layer API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def commerce_layer_source(client_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://yourdomain.commercelayer.io/api",
            "auth": {"type": "bearer", "token": client_credentials},
        },
        "resources": [
            {"name": "skus", "endpoint": {"path": "api/skus", "data_selector": "data"}},
            {"name": "shipments", "endpoint": {"path": "api/shipments", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_commerce_layer_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="commerce_layer_pipeline",
        destination="duckdb",
        dataset_name="commerce_layer_data",
    )
    load_info = pipeline.run(commerce_layer_source())
    print(load_info)


if __name__ == "__main__":
    load_commerce_layer_to_duckdb()

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

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How do I query Commerce Layer 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):

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import dlt

data = dlt.pipeline("commerce_layer_pipeline").dataset()
df = data.skus.df()
print(df.head())

SQL:

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SELECT * FROM commerce_layer_data.skus LIMIT 10;

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

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

Book a demo →

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What other destinations can I load Commerce Layer data to?

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

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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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack