Load React Router data to DuckDB
Build a React Router to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the React Router API base URL, auth, endpoints, and incremental loading.
React Router is a routing and framework library for React applications that allows developers to define custom RESTful resource routes as API endpoints within their own projects. Everything needed to build a working React Router → 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 React Router to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from React Router 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 React Router 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.
React Router API at a glance
| Base URL | N/A (Developers define their own routes and endpoints) |
| Example endpoint | GET api/users |
| Authentication | No standard authentication mechanism exists as it is a framework for custom implementations — sent in the Authorization header, prefixed Bearer |
| Pagination | Not paginated |
| API reference | https://reactrouter.com/how-to/middleware |
These values come from the React Router API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the React Router API?
React Router is a framework for building React applications, not a standalone REST API service; authentication is implemented by the developer within their own application code (e.g., using loaders or middleware).
No credentials required. The React Router API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.
What React Router data can I load into DuckDB?
These are the React Router endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| users | /api/users | GET | Fetch list of users | |
| user_details | /api/users/:id | GET | Fetch specific user | |
| graphql_api | /api/graphql | POST | GraphQL query endpoint | |
| auth_check | /api/auth/check | GET | Check authentication status | |
| health_check | /api/health | GET | Simple server health status |
How do I load only new React Router records?
The React Router 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": "users", "endpoint": { "path": "api/users", # 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 React Router pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading form and useNavigation from the React Router API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def react_router_source(): config: RESTAPIConfig = { "client": { "base_url": "N/A (Developers define their own routes and endpoints)", }, "resources": [ {"name": "users", "endpoint": {"path": "api/users"}}, {"name": "user_details", "endpoint": {"path": "api/users/:id"}} ], } yield from rest_api_resources(config) def load_react_router_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="react_router_pipeline", destination="duckdb", dataset_name="react_router_data", ) load_info = pipeline.run(react_router_source()) print(load_info) if __name__ == "__main__": load_react_router_to_duckdb()
Run it with python react_router_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 React Router 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("react_router_pipeline").dataset() df = data.users.df() print(df.head())
SQL:
SELECT * FROM react_router_data.users LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the React Router 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 React Router 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 React Router 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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