Load Eliftech Headless API data in Python using dltHub

Build a Eliftech Headless API-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete ElifTech 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 eliftech_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.eliftech.com/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            orders,,features,,suppliers
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Orders: Manage and retrieve order information.
  • Features: Access various features provided by the platform.
  • Suppliers: Interact with supplier data and functionalities.
  • User Panel: Access user-specific features and settings.
  • Super Apps: Explore functionalities related to super applications.
  • Wallets: Manage different types of wallets like NFT, IoT, and eWallets.
  • Trading: Access trading-related features and apps.
  • Banking: Interact with neobanking and open banking functionalities.
  • Returns: Manage return processes.

You will then debug the ElifTech 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 ElifTech support.

    dlt init dlthub:eliftech_migration 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 ElifTech API, as specified in @eliftech_migration-docs.yaml 
Start with endpoints orders and  and skip incremental loading for now. 
Place the code in eliftech_migration_pipeline.py and name the pipeline eliftech_migration_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 eliftech_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The source uses an API key for authentication, which is essential for accessing its features. Make sure to keep the API key secure and not expose it in public repositories.

    To get the appropriate API keys, please visit the original source at https://www.eliftech.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 eliftech_migration_pipeline.py

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

    Pipeline eliftech_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset eliftech_migration_data
The duckdb destination used duckdb:/eliftech_migration.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 eliftech_migration_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("eliftech_migration_pipeline").dataset()
# get rder table as Pandas frame
data.rder.df().head()

Running into errors?

Ensure that you have the necessary permissions and scopes defined in your OAuth setup. Rate limits can restrict the number of API calls, and it's advisable to handle potential API errors gracefully. Additionally, some objects may return nulls in deeply nested fields, and endpoint paths may change based on regulatory updates.

Extra resources:

Next steps