Load Yelp-leads data in Python using dltHub

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

In this guide, we'll set up a complete Yelp 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 yelp_leads_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.yelp.com/v3/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "categories",
            "opportunity",
            "business_reviews"
            ],
    }
    [...]
    yield from rest_api_resources(config)


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

  • Categories: Retrieve a list of business categories available on Yelp.
  • Opportunity: Create or manage business opportunity data.
  • Portfolio Project: Access information related to portfolio projects.
  • Business Reviews: Fetch reviews for specific businesses.
  • Businesses: Create new business entries in Yelp.
  • Restaurants: Get a list of partner restaurants available on Yelp.
  • Leads: Retrieve information about specific leads.
  • Photos: Access photos related to portfolio projects.
  • Business Subscriptions: Manage business subscription data on Yelp.
  • Events: Get information about events listed on Yelp.
  • Autocomplete: Suggest business names based on user input.
  • Daily Business Metrics: Request daily metrics for businesses.
  • Create Program: Initiate a program for a business.
  • Create Review Response: Respond to reviews posted about businesses.
  • Openings: Get reservation times for specified businesses.
  • Yelp Order: Create new orders through Yelp.
  • Monthly Business Report: Request monthly metrics for businesses.

You will then debug the Yelp 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 Yelp support.

    dlt init dlthub:yelp_leads 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 Yelp API, as specified in @yelp_leads-docs.yaml 
Start with endpoints "categories" and "opportunity" and skip incremental loading for now. 
Place the code in yelp_leads_pipeline.py and name the pipeline yelp_leads_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 yelp_leads_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is handled via OAuth2, requiring a bearer token in the header for requests. The token can be obtained via a refresh token flow, and details for obtaining the token are provided.

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

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

    Pipeline yelp_leads load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset yelp_leads_data
The duckdb destination used duckdb:/yelp_leads.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 yelp_leads_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("yelp_leads_pipeline").dataset()
# get "categories" table as Pandas frame
data.categories.df().head()

Running into errors?

It is essential to recognize that certain endpoints may require specific plan permissions (Enhanced or Premium) to access. Additionally, some APIs are deprecated and should be avoided, such as the Listing Management API. Moreover, constraints need to be checked in a specific order, and businesses without reviews cannot be retrieved through the API.

Extra resources:

Next steps