Load Tabby data to DuckDB

Tabby is a BNPL (Buy Now, Pay Later) payment gateway offering APIs for checkout, payments, and webhooks management. Everything needed to build a working Tabby → 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 Tabby 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 Tabby 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 Tabby 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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Tabby API at a glance

These values come from the Tabby API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Tabby API?

Tabby uses Bearer token authentication. You must include an 'Authorization' header in your request with the value 'Bearer <secret_key>', where <secret_key> is your merchant secret key.

1. Get your credentials

To obtain your API credentials, navigate to the Tabby Merchant Dashboard (merchant.tabby.ai for UAE/Kuwait or merchant.tabby.sa for KSA). Log in to your merchant account to access and copy your Secret and Public API keys. For custom integrations requiring production keys, these are typically provided by your Tabby account manager following a successful QA review. Testing keys (prefixed with pk_test_ / sk_test_) can often be retrieved directly from the dashboard or by contacting partner support.

2. Add them to .dlt/secrets.toml

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[sources.tabby_source]
api_key = "sk_test_..." # Replace with your actual Secret Key

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 Tabby data can I load into DuckDB?

These are the Tabby endpoints dlt can load into DuckDB:

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How do I load only new Tabby records?

Tabby exposes offset on api/v2/payments, 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": "payments", "endpoint": {
    "path": "api/v2/payments",
    "data_selector": "payments",
    "incremental": {"cursor_path": "offset", "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 Tabby pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading webhooks and payments from the Tabby API into DuckDB:

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

@dlt.source
def tabby_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.tabby.ai (for UAE, Kuwait) or https://api.tabby.sa (for KSA)",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "payments", "endpoint": {"path": "api/v2/payments", "data_selector": "payments"}},
            {"name": "disputes", "endpoint": {"path": "api/v1/disputes", "data_selector": "disputes"}}
        ],
    }
    yield from rest_api_resources(config)


def load_tabby_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="tabby_pipeline",
        destination="duckdb",
        dataset_name="tabby_data",
    )
    load_info = pipeline.run(tabby_source())
    print(load_info)


if __name__ == "__main__":
    load_tabby_to_duckdb()

Run it with python tabby_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 Tabby 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("tabby_pipeline").dataset()
df = data.payments.df()
print(df.head())

SQL:

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SELECT * FROM tabby_data.payments LIMIT 10;

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

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How do I deploy the Tabby 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 Tabby 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 Tabby 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