Load Judge.me data in Python using dltHub

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

In this guide, we'll set up a complete Judge.me 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 judge_me_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://judge.me/api/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            shops, events, reviews
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='judge_me_migration_pipeline',
        destination='duckdb',
        dataset_name='judge_me_migration_data', 
    )
    # Load the data
    load_info = pipeline.run(judge_me_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 judge_me_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:

  • Shops: Manage shop-related endpoints.
  • Reviews: Handle review operations including fetching and posting reviews.
  • Settings: Access configuration settings for the integration.
  • Reviewers: Manage reviewer information and operations.
  • Events: Monitor events related to reviews and other interactions.

You will then debug the Judge.me 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 Judge.me support.

    dlt init dlthub:judge_me_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 Judge.me API, as specified in @judge_me_migration-docs.yaml 
Start with endpoints shops and  and skip incremental loading for now. 
Place the code in judge_me_migration_pipeline.py and name the pipeline judge_me_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 judge_me_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Judge.me employs OAuth2 for authentication which requires a refresh token flow for secure access. A connected app must be set up within Judge.me to utilize this authentication method effectively. The access token is provided in the Authorization header for API requests.

    To get the appropriate API keys, please visit the original source at https://judge.me/. 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 judge_me_migration_pipeline.py

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

    Pipeline judge_me_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset judge_me_migration_data
The duckdb destination used duckdb:/judge_me_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 judge_me_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("judge_me_migration_pipeline").dataset()
# get hop table as Pandas frame
data.hop.df().head()

Running into errors?

Some endpoints may have rate limits, and certain responses might include deprecated fields not supported in the current version. Additionally, merchants need to ensure they have the necessary permissions set up in their connected app to avoid unauthorized errors. It is important to handle error responses effectively, particularly those related to authentication and rate limiting.

Extra resources:

Next steps