Load FEMA OpenFEMA data to DuckDB
Build a FEMA OpenFEMA to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the FEMA OpenFEMA API base URL, auth, endpoints, and incremental loading.
The OpenFEMA API provides public, read-only access to FEMA datasets via a RESTful interface using query string parameters. Everything needed to build a working FEMA OpenFEMA → 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 FEMA OpenFEMA to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from FEMA OpenFEMA 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 FEMA OpenFEMA 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.
FEMA OpenFEMA API at a glance
| Base URL | https://www.fema.gov/api/open |
| Example endpoint | GET v2/DisasterDeclarationsSummaries |
| Records found at | results |
| Authentication | the service does not require authentication |
| Pagination | Offset-based |
| Incremental field | lastRefresh |
| API reference | https://www.fema.gov/about/openfema/api |
These values come from the FEMA OpenFEMA API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the FEMA OpenFEMA API?
The OpenFEMA API does not require authentication, subscriptions, or API keys for access. All requests are public and free to use.
No credentials required. The FEMA OpenFEMA API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.
What FEMA OpenFEMA data can I load into DuckDB?
These are the FEMA OpenFEMA endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| disaster_declarations_summaries | v2/DisasterDeclarationsSummaries | GET | results | Disaster declaration summary records |
| disasters | v2/Disasters | GET | results | Disaster dataset listing disaster-level details |
| fema_regions | v2/FemaRegions | GET | results | FEMA regions |
| datasets | metadata/v3.0/DataSets | GET | results | Machine-readable list of available datasets |
| dataset_fields | metadata/v3.0/DataSetFields | GET | results | Field-level metadata for datasets |
How do I load only new FEMA OpenFEMA records?
FEMA OpenFEMA exposes lastRefresh on v2/DisasterDeclarationsSummaries, 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.
{"name": "disaster_declarations_summaries", "endpoint": { "path": "v2/DisasterDeclarationsSummaries", "data_selector": "results", "incremental": {"cursor_path": "lastRefresh", "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 FEMA OpenFEMA pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading DataSets and DataSetFields from the FEMA OpenFEMA API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def fema_openfema_source(): config: RESTAPIConfig = { "client": { "base_url": "https://www.fema.gov/api/open", }, "resources": [ {"name": "disaster_declarations_summaries", "endpoint": {"path": "v2/DisasterDeclarationsSummaries", "data_selector": "results"}}, {"name": "disasters", "endpoint": {"path": "v2/Disasters", "data_selector": "results"}} ], } yield from rest_api_resources(config) def load_fema_openfema_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="fema_openfema_pipeline", destination="duckdb", dataset_name="fema_openfema_data", ) load_info = pipeline.run(fema_openfema_source()) print(load_info) if __name__ == "__main__": load_fema_openfema_to_duckdb()
Run it with python fema_openfema_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 FEMA OpenFEMA 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("fema_openfema_pipeline").dataset() df = data.disaster_declarations_summaries.df() print(df.head())
SQL:
SELECT * FROM fema_openfema_data.disaster_declarations_summaries LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the FEMA OpenFEMA 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 FEMA OpenFEMA 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 FEMA OpenFEMA 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.
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