Load Retail Express data to DuckDB

Retail Express is a RESTful API platform that provides access to Retail Express POS and retail management data such as products, customers, orders, outlets, inventory, and financial summaries for integrations. Everything needed to build a working Retail Express → 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 Retail Express 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 Retail Express 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 Retail Express 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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Retail Express API at a glance

These values come from the Retail Express API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Retail Express API?

Authentication requires a two-step process: first, exchange an API key (passed via the x-api-key header) for a short-lived Bearer access token, then include that token in the Authorization header along with the x-api-key header for all subsequent API requests. The access token expires after 60 minutes.

1. Get your credentials

1. Log in to your Retail Express account with an administrator user.\n2. Navigate to Settings > Integrations > API Management.\n3. Enter a descriptive name in the API Key Name field.\n4. Enter a description for the integration in the API Key Description field (if required).\n5. Select Enabled.\n6. Click Generate Keys.\n7. Copy either the Primary Key or the Secondary Key. Both keys are functionally identical. Store this key securely as it is sensitive. Note: You must have the 'API Management' security permission enabled in your staff profile to access this section. Contact Retail Express sales if you require a license to access the API.

2. Add them to .dlt/secrets.toml

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[sources.retail_express_source]
api_key = "REPLACE_ME"

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 Retail Express data can I load into DuckDB?

These are the Retail Express endpoints dlt can load into DuckDB:

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How do I load only new Retail Express records?

The Retail Express API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "products", "endpoint": {
    "path": "v2.1/products",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Retail Express pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /auth/token and /products from the Retail Express API into DuckDB:

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

@dlt.source
def retail_express_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.retailexpress.com.au",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "v2.1/products", "data_selector": "products"}},
            {"name": "orders", "endpoint": {"path": "v2.1/orders", "data_selector": "orders"}}
        ],
    }
    yield from rest_api_resources(config)


def load_retail_express_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="retail_express_pipeline",
        destination="duckdb",
        dataset_name="retail_express_data",
    )
    load_info = pipeline.run(retail_express_source())
    print(load_info)


if __name__ == "__main__":
    load_retail_express_to_duckdb()

Run it with python retail_express_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 Retail Express 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("retail_express_pipeline").dataset()
df = data.products.df()
print(df.head())

SQL:

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SELECT * FROM retail_express_data.products LIMIT 10;

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

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How do I deploy the Retail Express 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 Retail Express 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 Retail Express 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