Hilti on!track Python API Docs | dltHub

ON!Track Unite is a RESTful asset and workforce management API platform for managing assets, locations, workers, services, transfers, documents and related domain models. The REST API base URL is https://unite.ontrack3.hilti.com/unite/v1 and OAuth2 (authorization code / client credentials) or API client onboarding; requests require authentication and use Authorization header with a Bearer token..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Hilti on!track data in under 10 minutes.

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What data can I load from Hilti on!track?

Here are some of the endpoints you can load from Hilti on!track:

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How do I authenticate with the Hilti on!track API?

ON!Track Unite uses OAuth2 for onboarding external applications. Obtain client credentials via the Unite developer onboarding flow; include the access token in requests as: Authorization: Bearer . All traffic is over HTTPS and payloads are JSON.

1. Get your credentials

1. Open the developer hub: https://unite.ontrack3.hilti.com/developer. 2) Follow the onboarding/auth guide (Onboarding and authorization) to register an application and request API access. 3) Choose an OAuth2 flow (authorization code for user delegation or client credentials for machine access) and note client_id and client_secret. 4) Exchange credentials at the token endpoint (per the guide) to obtain an access token. 5) Use the access token in Authorization header for subsequent API calls.

2. Add them to .dlt/secrets.toml

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[sources.hilti_ontrack_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Hilti on!track API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python hilti_ontrack_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline hilti_ontrack_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hilti_ontrack_data
The duckdb destination used duckdb:/hilti_ontrack.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline hilti_ontrack_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads assets and workers from the Hilti on!track API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def hilti_ontrack_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://unite.ontrack3.hilti.com/unite/v1",
            "auth": {
                "type": "bearer",
                "token": client_secret,
            },
        },
        "resources": [
            {"name": "assets", "endpoint": {"path": "assets", "data_selector": "result"}},
            {"name": "workers", "endpoint": {"path": "workers", "data_selector": "result"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hilti_ontrack_pipeline",
        destination="duckdb",
        dataset_name="hilti_ontrack_data",
    )
    load_info = pipeline.run(hilti_ontrack_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("hilti_ontrack_pipeline").dataset()
sessions_df = data.assets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hilti_ontrack_data.assets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hilti_ontrack_pipeline").dataset()
data.assets.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Hilti on!track data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If the Authorization header is missing, expired, or invalid the API returns 401 responses (Problem JSON). Obtain a fresh access token via the OAuth2 token endpoint and retry. Ensure client_id/client_secret are correct and token has not expired.

Rate limiting and throttling

When throttling limits are reached a 429 status is returned. Back off and retry after a delay. Use pagination (offset, limit) to reduce large requests.

Pagination and collection envelope

List endpoints return a metadata envelope with keys: result (array of records), offset, limit, totalRecords. Use offset and limit query params (defaults offset=0 limit=50) to page through results.

Error format

Errors follow RFC7807 Problem JSON with fields type, title, status, detail, instance and an errors array with per-field codes and messages.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack