Load Katana MRP data to DuckDB
Build a Katana MRP to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Katana MRP API base URL, auth, endpoints, and incremental loading.
Katana MRP is a manufacturing resource planning platform that offers a REST API for managing products, inventory, orders, and other manufacturing data. Everything needed to build a working Katana MRP → 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 Katana MRP to DuckDB pipeline
Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.
PromptRunuvx dlthub-init@latestto build a pipeline from Katana MRP 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 Katana MRP 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.
Katana MRP API at a glance
| Base URL | https://api.katanamrp.com/v1 |
| Example endpoint | GET sales_orders |
| Authentication | All requests require a Bearer token in the Authorization header — sent in the Authorization header, prefixed Bearer |
| Pagination | Page-number via page, page size via limit (default 50, max 250). Katana uses offset-style pagination controlled by the page (requested page number) and limit (page size) query parameters. Default page is 1. Pagination metadata is returned via response headers (e.g., X-Pagination); there is no cursor token parameter in the API docs. |
| Incremental field | updated_at |
| Record id | id |
| API reference | https://developer.katanamrp.com/reference/api-authentication |
These values come from the Katana MRP API reference — the authoritative source if anything here looks out of date.
How do I authenticate with the Katana MRP API?
Katana MRP uses token-based authentication where API keys are passed in the 'Authorization' header with the 'Bearer' prefix. All requests must include this header in the format: 'Authorization: Bearer <API_KEY>'.
1. Get your credentials
- Log in to your Katana account at https://katanamrp.com.\n2. Navigate to Settings > API (or Settings > API > API keys).\n3. Click + Add new API key.\n4. Provide a name/description for the key and configure scopes if required.\n5. Save the key and copy the generated value immediately; it will be used as a Bearer token in the 'Authorization' header of your API requests. Ensure this key is kept secure and never exposed in client-side code.
2. Add them to .dlt/secrets.toml
[sources.katana_mrp_source] api_key = "your_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 Katana MRP data can I load into DuckDB?
These are the Katana MRP endpoints dlt can load into DuckDB:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| sales_orders | sales_orders | GET | List all sales orders | |
| products | products | GET | List all products | |
| inventory_movements | inventory_movements | GET | List all inventory movements | |
| locations | locations | GET | List all locations | |
| manufacturing_orders | manufacturing_orders | GET | List all manufacturing orders |
How do I load only new Katana MRP records?
Katana MRP exposes updated_at on sales_orders, 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": "sales_orders", "endpoint": { "path": "sales_orders", "incremental": {"cursor_path": "updated_at", "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 Katana MRP pipeline look like?
A standard dlt REST API pipeline — the same code you would write by hand, loading products and sales_orders from the Katana MRP API into DuckDB:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def katana_mrp_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.katanamrp.com/v1", "auth": {"type": "bearer", "token": api_key}, }, "resources": [ {"name": "sales_orders", "endpoint": {"path": "sales_orders"}}, {"name": "products", "endpoint": {"path": "products"}} ], } yield from rest_api_resources(config) def load_katana_mrp_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="katana_mrp_pipeline", destination="duckdb", dataset_name="katana_mrp_data", ) load_info = pipeline.run(katana_mrp_source()) print(load_info) if __name__ == "__main__": load_katana_mrp_to_duckdb()
Run it with python katana_mrp_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 Katana MRP 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("katana_mrp_pipeline").dataset() df = data.products.df() print(df.head())
SQL:
SELECT * FROM katana_mrp_data.products LIMIT 10;
See querying your data with dataset and exploring it in marimo notebooks.
How do I deploy the Katana MRP 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 Katana MRP 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 Katana MRP 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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