Load Bridge API data to DuckDB

Bridge API is a financial aggregation and payments platform providing access to banking and transaction data. Everything needed to build a working Bridge API → 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 Bridge API 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 Bridge API 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 Bridge API 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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Bridge API API at a glance

These values come from the Bridge API API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Bridge API API?

Authentication for the Bridge API (bridgeapi.io) involves a two-step process where Client-Id and Client-Secret headers are used to obtain an access_token, which is then provided in the Authorization header as a Bearer token. Headers include 'Bridge-Version: 2025-01-15' and 'Content-Type: application/json'.

1. Get your credentials

1. Navigate to the Bridge Dashboard at https://dashboard.bridge.xyz and log in to your account. 2. Once logged in, click on the 'API Keys' tab located on the top menu bar. 3. Click the button to generate a new API key. 4. Immediately copy and store the API key securely. It is displayed only once at the time of creation and cannot be retrieved later.

2. Add them to .dlt/secrets.toml

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[sources.bridge_api_source]
api_key = "sk_live_..."

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 Bridge API data can I load into DuckDB?

These are the Bridge API endpoints dlt can load into DuckDB:

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How do I load only new Bridge API records?

The Bridge API 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": "transactions", "endpoint": {
    "path": "v3/aggregation/transactions",
    # 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 Bridge API pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api_keys and /customers from the Bridge API API into DuckDB:

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

@dlt.source
def bridge_api_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.bridgeapi.io/v3",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "v3/aggregation/transactions", "data_selector": "resources"}},
            {"name": "transfers", "endpoint": {"path": "v0/transfers", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_bridge_api_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bridge_api_pipeline",
        destination="duckdb",
        dataset_name="bridge_api_data",
    )
    load_info = pipeline.run(bridge_api_source())
    print(load_info)


if __name__ == "__main__":
    load_bridge_api_to_duckdb()

Run it with python bridge_api_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 Bridge API 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("bridge_api_pipeline").dataset()
df = data.transactions.df()
print(df.head())

SQL:

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SELECT * FROM bridge_api_data.transactions LIMIT 10;

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

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How do I deploy the Bridge API 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 Bridge API 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 Bridge API 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