Load Trados Cloud Platform data in Python using dltHub

Build a Trados Cloud Platform-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Trados Cloud Platform 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 trados_cloud_platform_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sdl-prod.eu.auth0.com/oauth/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            token
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='trados_cloud_platform_pipeline',
        destination='duckdb',
        dataset_name='trados_cloud_platform_data', 
    )
    # Load the data
    load_info = pipeline.run(trados_cloud_platform_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 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 trados_cloud_platform’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Authentication: OAuth token endpoint for obtaining access credentials
  • Cloud Services: EU-based cloud platform for Trados services
  • API Gateway: Main API endpoint for SDL product integrations and services

You will then debug the Trados Cloud Platform 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 Trados Cloud Platform support.

    dlt init dlthub:trados_cloud_platform 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 Trados Cloud Platform API, as specified in @trados_cloud_platform-docs.yaml 
Start with endpoint(s) token and skip incremental loading for now. 
Place the code in trados_cloud_platform_pipeline.py and name the pipeline trados_cloud_platform_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 trados_cloud_platform_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The Trados Cloud Platform API uses Bearer token authentication obtained from Auth0. The access token is provided in the access_token property of the Auth0 response and must be included in the Authorization header as Bearer {{access_token}}. Additionally, the X-LC-Tenant header with the tenant ID must be provided in every request. Tokens expire after the duration specified in the expires_in property (in seconds) and should be cached and reused; applications should request fresh tokens before expiry and limit token requests to a maximum of 16 per day.

    To get the appropriate API keys, please visit the original source at eu.cloud.trados.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 trados_cloud_platform_pipeline.py

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

    Pipeline trados_cloud_platform load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset trados_cloud_platform_data
The duckdb destination used duckdb:/trados_cloud_platform.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 trados_cloud_platform_pipeline show

  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("trados_cloud_platform_pipeline").dataset()
# get token table as Pandas frame
data.token.df().head()

Extra resources:

Next steps