Load Hilti on!track data in Python using dltHub

Build a Hilti on!track-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Hilti data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def hilti_ontrack_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://your-instance.api-name.com/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "assets",
            "documents",
            "employees"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='hilti_ontrack_pipeline',
        destination='duckdb',
        dataset_name='hilti_ontrack_data', 
    )
    # Load the data
    load_info = pipeline.run(hilti_ontrack_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from hilti_ontrack’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Assets: Manage and retrieve asset information, including costs and details.
  • Documents: Handle document uploads and retrievals, including pre-signed URLs.
  • Employees: Update and manage employee information.
  • Locations: Manage location data related to assets.
  • Usage History: Access usage history for single or multiple assets.
  • Runtime Data: Retrieve runtime data for assets, either singularly or in bulk.

You will then debug the Hilti pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with Hilti support.

    dlt init dlthub:hilti_ontrack duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Hilti API, as specified in @hilti_ontrack-docs.yaml 
Start with endpoints "assets" and "documents" and skip incremental loading for now. 
Place the code in hilti_ontrack_pipeline.py and name the pipeline hilti_ontrack_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python hilti_ontrack_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is done via OAuth2, requiring a client ID, client secret, username, and password for a technical user to obtain a bearer token.

    To get the appropriate API keys, please visit the original source at https://www.hilti.com/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python hilti_ontrack_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline hilti_ontrack 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

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline hilti_ontrack_pipeline show --dashboard

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("hilti_ontrack_pipeline").dataset()
# get "assets" table as Pandas frame
data.assets.df().head()

Running into errors?

Developers must handle the token securely, as it is valid for only 60 minutes and requires renewal. Additionally, certain APIs are exclusively available for specific customer tiers, and there are limitations on the timeframe for which data can be queried. Performance issues have been noted with some endpoints, and developers should be aware of the need for proper error handling.

Extra resources:

Next steps