Load Plaid - Check data to DuckDB

Plaid is a financial data infrastructure platform that provides REST APIs to connect to financial institutions for account and transaction data accessing. Everything needed to build a working Plaid - Check → 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 Plaid - Check 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 Plaid - Check 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 Plaid - Check 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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Plaid - Check API at a glance

These values come from the Plaid - Check API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Plaid - Check API?

Authentication is performed by providing a client_id and secret, which can be sent as headers (PLAID-CLIENT-ID, PLAID-SECRET) or included in the JSON request body. Requests must be sent over HTTPS (TLS v1.2) and include a Content-Type header set to application/json.

1. Get your credentials

1. Create a Plaid developer account at the Plaid Dashboard (https://dashboard.plaid.com/signup). 2. Log in and navigate to the 'Developers' menu. 3. Select 'API keys' to view your 'client_id' and 'secret' for the Sandbox and Production environments. 4. Complete your application and company profiles in the 'Settings' section of the dashboard before connecting to production institutions.

2. Add them to .dlt/secrets.toml

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[sources.plaid_check_source]
client_id = "REPLACE_ME"

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 Plaid - Check data can I load into DuckDB?

These are the Plaid - Check endpoints dlt can load into DuckDB:

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How do I load only new Plaid - Check records?

Plaid - Check exposes cursor on transactions/sync, 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": "transactions_sync", "endpoint": {
    "path": "transactions/sync",
    "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 Plaid - Check pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /accounts/get and /transactions/get from the Plaid - Check API into DuckDB:

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

@dlt.source
def plaid_check_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://production.plaid.com",
            "auth": {"type": "api_key", "api_key": client_id, "name": "client_id"},
        },
        "resources": [
            {"name": "transactions_sync", "endpoint": {"path": "transactions/sync"}},
            {"name": "accounts_get", "endpoint": {"path": "accounts/get"}}
        ],
    }
    yield from rest_api_resources(config)


def load_plaid_check_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="plaid_check_pipeline",
        destination="duckdb",
        dataset_name="plaid_check_data",
    )
    load_info = pipeline.run(plaid_check_source())
    print(load_info)


if __name__ == "__main__":
    load_plaid_check_to_duckdb()

Run it with python plaid_check_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 Plaid - Check 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("plaid_check_pipeline").dataset()
df = data.transactions_sync.df()
print(df.head())

SQL:

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SELECT * FROM plaid_check_data.transactions_sync LIMIT 10;

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

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How do I deploy the Plaid - Check 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 Plaid - Check 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 Plaid - Check 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