Load PTC Windchill data to DuckDB

PTC Windchill REST Services provides OData-compliant APIs to interact with Windchill product lifecycle management data. Everything needed to build a working PTC Windchill → 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.

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Build your PTC Windchill 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 PTC Windchill 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 PTC Windchill 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.

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PTC Windchill API at a glance

These values come from the PTC Windchill API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the PTC Windchill API?

Windchill supports OAuth 2.0 where the access token is provided as a Bearer token in the 'Authorization' header. Alternatively, HTTP Basic authentication or session cookies can be used for standard API access.

1. Get your credentials

PTC Windchill REST Services (WRS) typically utilizes OAuth 2.0 for authentication rather than static API keys. To obtain credentials: 1. Ensure your Windchill instance is configured as an OAuth Resource Server and your application is registered as a Service Provider with your Central Authorization Server (CAS). 2. Direct your application to the authorization endpoint to obtain an authorization code. 3. Exchange the authorization code for an access token by sending a POST request (Content-Type: application/x-www-form-urlencoded) to the CAS token endpoint containing your client_id, client_secret, grant_type=authorization_code, and the authorization code. 4. Use the returned access_token as a Bearer Token in the Authorization header of your WRS API requests. Note that endpoints requiring OAuth must typically be accessed via URLs containing an 'oauth' prefix.

2. Add them to .dlt/secrets.toml

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[sources.ptc_windchill_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
token_url = "https://your-cas-server.com/token"
authorization_url = "https://your-cas-server.com/auth"

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.

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What PTC Windchill data can I load into DuckDB?

These are the PTC Windchill endpoints dlt can load into DuckDB:

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How do I load only new PTC Windchill records?

PTC Windchill exposes id on ProdMgmt/Parts, 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.

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{"name": "parts", "endpoint": {
    "path": "ProdMgmt/Parts",
    "data_selector": "value",
    "incremental": {"cursor_path": "id", "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.

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What does the generated PTC Windchill pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading Parts and Documents from the PTC Windchill API into DuckDB:

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

@dlt.source
def ptc_windchill_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<windchill-host>/Windchill/servlet/odata/v5",
            "auth": {"type": "bearer", "token": username},
        },
        "resources": [
            {"name": "parts", "endpoint": {"path": "ProdMgmt/Parts", "data_selector": "value"}},
            {"name": "documents", "endpoint": {"path": "DocMgmt/Documents", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_ptc_windchill_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ptc_windchill_pipeline",
        destination="duckdb",
        dataset_name="ptc_windchill_data",
    )
    load_info = pipeline.run(ptc_windchill_source())
    print(load_info)


if __name__ == "__main__":
    load_ptc_windchill_to_duckdb()

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

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How do I query PTC Windchill 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):

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import dlt

data = dlt.pipeline("ptc_windchill_pipeline").dataset()
df = data.parts.df()
print(df.head())

SQL:

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SELECT * FROM ptc_windchill_data.parts LIMIT 10;

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

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

Book a demo →

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What other destinations can I load PTC Windchill 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.

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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