Load Dojo data in Python using dltHub

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

In this guide, we'll set up a complete Dojo 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 dojo_epos_data_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://virtserver.swaggerhub.com/DojoPaymentProducts/EPOS-Gateway-API/1.0/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "reservations", "orders"
        ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Reservations: Endpoints related to managing reservations, including creating, searching, and retrieving specific reservations.
  • Orders: Endpoints for handling orders, allowing for creation, searching, and retrieving specific orders.
  • Areas: Endpoints that provide information about different areas within the system, likely related to seating or layout.
  • Tables: Endpoints for searching and retrieving information about tables, likely for managing seating arrangements.
  • Parties: Endpoints for managing parties, including searching for specific parties and retrieving details about them.

You will then debug the Dojo 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 Dojo support.

    dlt init dlthub:dojo_epos_data 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 Dojo API, as specified in @dojo_epos_data-docs.yaml 
Start with endpoints reservations and orders and skip incremental loading for now. 
Place the code in dojo_epos_data_pipeline.py and name the pipeline dojo_epos_data_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 dojo_epos_data_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    You need to use the latest Dojo API keys, which can be obtained from the developer portal under Dojo API keys, and for the test environment, use your secret key with the prefix sk_sandbox_. Additionally, if you're using old APIs, you can retrieve the JWT token from the REMOTE PAYMENTS > Developer resources tab in the developer portal.

    To get the appropriate API keys, please visit the original source at https://docs.dojo.tech/api-hosting/epos-data. 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 dojo_epos_data_pipeline.py

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

    Pipeline dojo_epos_data load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dojo_epos_data_data
The duckdb destination used duckdb:/dojo_epos_data.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 dojo_epos_data_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("dojo_epos_data_pipeline").dataset()
# get "reservations" table as Pandas frame
data."reservations".df().head()

Extra resources:

Next steps