Load Toast data to DuckDB

Toast APIs provide access to platform resources for restaurant integration partners using OAuth 2 client-credentials authentication. Everything needed to build a working Toast → 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 Toast 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 Toast 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 Toast 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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Toast API at a glance

These values come from the Toast API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Toast API?

Authentication requires an OAuth 2 bearer token obtained from the authentication API, which must be passed in the Authorization HTTP header with the 'Bearer ' prefix. Additionally, requests typically require the 'Toast-Restaurant-External-ID' header to specify the restaurant context.

1. Get your credentials

For partner-level integrations, your account is created during onboarding, and credentials are managed via the Toast Developer Portal. Navigate to the Credentials page to view or rotate your Client ID and Client Secret. For standard API access, log in to Toast Web, navigate to Integrations > Toast API access > Manage credentials, select Create new credentials > Standard API, define your required scopes and locations, and save.

2. Add them to .dlt/secrets.toml

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[sources.toast_source]
toast_client_id = "your_client_id_here"
toast_client_secret = "your_client_secret_here"
toast_user_access_type = "TOAST_MACHINE_CLIENT"

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 Toast data can I load into DuckDB?

These are the Toast endpoints dlt can load into DuckDB:

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How do I load only new Toast records?

Toast exposes modifiedDate on orders/v2/ordersBulk, 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": "orders", "endpoint": {
    "path": "orders/v2/ordersBulk",
    "incremental": {"cursor_path": "modifiedDate", "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 Toast pipeline look like?

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

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

@dlt.source
def toast_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Toast API hostnames are environment-specific (e.g., sandbox vs. production) and are provided by the Toast integrations team upon registration.",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "orders", "endpoint": {"path": "orders/v2/ordersBulk"}},
            {"name": "employees", "endpoint": {"path": "labor/v1/employees"}}
        ],
    }
    yield from rest_api_resources(config)


def load_toast_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="toast_pipeline",
        destination="duckdb",
        dataset_name="toast_data",
    )
    load_info = pipeline.run(toast_source())
    print(load_info)


if __name__ == "__main__":
    load_toast_to_duckdb()

Run it with python toast_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 Toast 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("toast_pipeline").dataset()
df = data.ordersBulk.df()
print(df.head())

SQL:

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SELECT * FROM toast_data.ordersBulk LIMIT 10;

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

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How do I deploy the Toast 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 Toast 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 Toast 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