Load Fed Treasury data to DuckDB

The U.S. Treasury Fiscal Data API provides open, public access to federal financial data including national debt, interest rates, and fiscal operations data. Everything needed to build a working Fed Treasury → 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.

---

Build your Fed Treasury 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 Fed Treasury 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 Fed Treasury 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.

---

Fed Treasury API at a glance

These values come from the Fed Treasury API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the Fed Treasury API?

The Fiscal Data API is public and does not require authentication for requests, although an API key can optionally be included as a query parameter for higher rate limits.

1. Get your credentials

The U.S. Treasury's public Fiscal Data API is open and does not require a user account or API key for basic GET requests. You can access it directly via the base URL: https://api.fiscaldata.treasury.gov/services/api/fiscal_service/. If your application requires higher rate limits, you may optionally include an api_key as a query parameter, which can be requested by contacting the Fiscal Data team through their official website's Contact/FAQ page.

2. Add them to .dlt/secrets.toml

Copy
[sources.fed_treasury_source]
# api_key = "optional_api_key_if_requested"

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.

---

What Fed Treasury data can I load into DuckDB?

These are the Fed Treasury endpoints dlt can load into DuckDB:

---

How do I load only new Fed Treasury records?

Fed Treasury exposes page[number] on services/api/fiscal_service, 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.

Copy
{"name": "fiscal_data_api", "endpoint": {
    "path": "services/api/fiscal_service",
    "data_selector": "data",
    "incremental": {"cursor_path": "page[number]", "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.

---

What does the generated Fed Treasury pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v1/accounting/od/rates_of_exchange and v2/accounting/od/debt_to_penny from the Fed Treasury API into DuckDB:

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

@dlt.source
def fed_treasury_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.fiscaldata.treasury.gov/services/api/fiscal_service/",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "fiscal_data_api", "endpoint": {"path": "services/api/fiscal_service", "data_selector": "data"}},
            {"name": "treasury_statements", "endpoint": {"path": "v1/accounting/dts/dts_table_1", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_fed_treasury_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fed_treasury_pipeline",
        destination="duckdb",
        dataset_name="fed_treasury_data",
    )
    load_info = pipeline.run(fed_treasury_source())
    print(load_info)


if __name__ == "__main__":
    load_fed_treasury_to_duckdb()

Run it with python fed_treasury_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query Fed Treasury 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):

Copy
import dlt

data = dlt.pipeline("fed_treasury_pipeline").dataset()
df = data.daily_treasury_statement.df()
print(df.head())

SQL:

Copy
SELECT * FROM fed_treasury_data.daily_treasury_statement LIMIT 10;

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

---

How do I deploy the Fed Treasury 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 Fed Treasury loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load Fed Treasury 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.

---

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