Load Census Data data to DuckDB
Build a Census Data to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Census Data API base URL, auth, endpoints, and incremental loading.
The Census Data API provides public access to raw statistical data from various U.S. Census Bureau datasets. Everything needed to build a working Census Data → 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 Census Data to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Census Data 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 Census Data 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.
Census Data API at a glance
| Base URL | https://api.census.gov/data |
| Example endpoint | GET data.json |
| Authentication | All data requests require an API key passed as a query parameter — sent in the request query |
| Pagination | Not paginated |
| API reference | https://www.census.gov/data/developers/guidance/api-user-guide.html |
These values come from the Census Data API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Census Data API?
The API uses an API key passed as a query string parameter named 'key' (e.g., &key=your_key_here) appended to the end of the request URL.
1. Get your credentials
- Navigate to the official U.S. Census Bureau Developers portal at https://www.census.gov/developers/. 2. Locate and click on the 'Request a KEY' box. 3. Complete the form provided in the pop-up window; ensure the email address uses an approved domain (.com, .net, .org, .gov, or .edu). 4. Check your email for the confirmation message containing your API key code and the registration activation link.
2. Add them to .dlt/secrets.toml
[sources.census_data_source] api_key = "your_actual_api_key_here"
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 Census Data data can I load into DuckDB?
These are the Census Data endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| datasets | /data.json | GET | Returns a list of all available Census API datasets. | |
| variables | /data/{year}/{dataset_id}/variables.json | GET | Returns metadata/list of variables for a specific dataset. | |
| geographies | /data/{year}/{dataset_id}/geographies.json | GET | Returns metadata/list of geographic areas available for a dataset. | |
| groups | /data/{year}/{dataset_id}/groups.json | GET | Returns metadata/list of variable groups for a dataset. | |
| values | /data/{year}/{dataset_id}/{endpoint} | GET | Main endpoint to query statistical data using predicates. |
How do I load only new Census Data records?
The Census Data 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.
{"name": "datasets", "endpoint": { "path": "data.json", # 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.
What does the generated Census Data pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading /data/{year}/{dataset} and /data/{year}/{dataset}/variables.json from the Census Data API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def census_data_source(key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.census.gov/data", "auth": {"type": "api_key", "api_key": key, "name": "key", "location": "query"}, }, "resources": [ {"name": "datasets", "endpoint": {"path": "data.json"}}, {"name": "values", "endpoint": {"path": "data/{year}/{dataset_id}/{dataset_endpoint}"}} ], } yield from rest_api_resources(config) def load_census_data_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="census_data_pipeline", destination="duckdb", dataset_name="census_data_data", ) load_info = pipeline.run(census_data_source()) print(load_info) if __name__ == "__main__": load_census_data_to_duckdb()
Run it with python census_data_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 Census Data 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):
import dlt data = dlt.pipeline("census_data_pipeline").dataset() df = data.values.df() print(df.head())
SQL:
SELECT * FROM census_data_data.values LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Census Data 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 Census Data loads into governed, documented models.
- Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.
What other destinations can I load Census Data data to?
dlt loads into any of these — only the destination argument changes:
| Destination | Example value |
|---|---|
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
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
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