Load Printful data to DuckDB

Printful is a print-on-demand and fulfillment platform providing a RESTful API to manage stores, products, orders, shipping, and related resources. Everything needed to build a working Printful → 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 Printful 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 Printful 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 Printful 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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Printful API at a glance

These values come from the Printful API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Printful API?

Authentication is handled by passing a private token or OAuth 2.0 access token as a Bearer token in the Authorization header. When using account-level tokens, the target store ID must be provided via the X-PF-Store-Id header.

1. Get your credentials

1. Log in to your Printful Dashboard. 2. Navigate to Settings, then select API (or look for the Developers section). 3. Click to create a new token. 4. Provide a name, optional email, set an expiration date, and assign the required access scopes for your store. 5. Click Create new token and copy the generated token immediately, as it cannot be viewed again.

2. Add them to .dlt/secrets.toml

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[sources.printful_source]
api_key = "your_printful_api_key_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 Printful data can I load into DuckDB?

These are the Printful endpoints dlt can load into DuckDB:

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How do I load only new Printful records?

The Printful 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": "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 Printful pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/orders and /v2/products from the Printful API into DuckDB:

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

@dlt.source
def printful_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.printful.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "products", "data_selector": "result"}},
            {"name": "sync_products", "endpoint": {"path": "store/products", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def load_printful_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="printful_pipeline",
        destination="duckdb",
        dataset_name="printful_data",
    )
    load_info = pipeline.run(printful_source())
    print(load_info)


if __name__ == "__main__":
    load_printful_to_duckdb()

Run it with python printful_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 Printful 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("printful_pipeline").dataset()
df = data.products.df()
print(df.head())

SQL:

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SELECT * FROM printful_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 Printful 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 Printful 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 Printful 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