Load Stark Bank data to DuckDB

Stark Bank is a banking API platform that provides REST endpoints for financial operations including transfers, transactions, and webhooks. Everything needed to build a working Stark Bank → 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 Stark Bank 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 Stark Bank 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 Stark Bank 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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Stark Bank API at a glance

These values come from the Stark Bank API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Stark Bank API?

All API requests must be signed using ECDSA digital signatures (secp256k1 curve) with SHA-256 digest. Requests require three specific headers: Access-Id, Access-Time, and Access-Signature.

1. Get your credentials

Stark Bank does not use traditional API keys or access tokens. Instead, authentication is performed using ECDSA digital signatures (secp256k1 curve, SHA-256 digest). 1. Generate an ECDSA private/public key pair (using the SDK or openssl). 2. Log into the Stark Bank Web Banking dashboard (Sandbox or Production). 3. Register your generated public key within the dashboard's credentials/public key settings. 4. Use your Project ID (or Organization ID) and the private key (stored securely, not hardcoded) to authenticate requests. For dlt integrations, the private key in PEM format and your Project/Organization ID are required.

2. Add them to .dlt/secrets.toml

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[sources.stark_bank_source]
access_id = "project/your_project_id"
private_key_pem = "-----BEGIN EC PRIVATE KEY-----\n...\n-----END EC PRIVATE 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 Stark Bank data can I load into DuckDB?

These are the Stark Bank endpoints dlt can load into DuckDB:

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How do I load only new Stark Bank records?

Stark Bank exposes cursor on v2/transfer, 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": "transfer", "endpoint": {
    "path": "v2/transfer",
    "data_selector": "transfers",
    "incremental": {"cursor_path": "cursor", "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 Stark Bank pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v2/transfer and v2/public-key from the Stark Bank API into DuckDB:

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

@dlt.source
def stark_bank_source(private_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.starkbank.com/v2 (production) and https://sandbox.api.starkbank.com/v2 (sandbox)",
            "auth": {"type": "api_key", "api_key": private_key, "name": "private_key"},
        },
        "resources": [
            {"name": "transfer", "endpoint": {"path": "v2/transfer", "data_selector": "transfers"}},
            {"name": "transaction", "endpoint": {"path": "v2/transaction", "data_selector": "transactions"}}
        ],
    }
    yield from rest_api_resources(config)


def load_stark_bank_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="stark_bank_pipeline",
        destination="duckdb",
        dataset_name="stark_bank_data",
    )
    load_info = pipeline.run(stark_bank_source())
    print(load_info)


if __name__ == "__main__":
    load_stark_bank_to_duckdb()

Run it with python stark_bank_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 Stark Bank 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("stark_bank_pipeline").dataset()
df = data.transaction.df()
print(df.head())

SQL:

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SELECT * FROM stark_bank_data.transaction LIMIT 10;

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

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How do I deploy the Stark Bank 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 Stark Bank 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 Stark Bank 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