Uber Eats Data API: Extract Structured JSON in 2026

TL;DR: To get structured Uber Eats data, use a schema-based extraction API. Instead of writing brittle CSS selectors, you pass a JSON schema to an endpoint, and the API returns typed, validated data from the public page.

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Building data pipelines for food delivery platforms is notoriously difficult due to highly dynamic DOM structures and aggressive anti-bot measures. If you are trying to build a market intelligence tool or a local food aggregator, you don't need a scraper; you need a reliable data API.

Why Extract Uber Eats Data?

Engineering teams often require real-time food delivery metrics for several high-value use cases:

• Market Intelligence: Track restaurant density and cuisine trends in specific geographic regions.
• Competitive Benchmarking: Monitor delivery times and pricing fluctuations across different service areas.
• AI Training & RAG: Feed up-to-date restaurant metadata into LLMs to power food discovery agents.
• Real Estate Analytics: Use restaurant density and delivery popularity to inform commercial real estate decisions.

What Data Can You Extract?

When building your pipeline, you should focus on publicly available attributes. Since we use schema-based extraction, you can define exactly what you need. Common fields include:

• restaurant_name: The legal or display name of the establishment.
• cuisine_type: The category of food (e.s., Italian, Sushi, Vegan).
• rating: The numerical user rating.
• delivery_time: Estimated arrival window.
• min_order_amount: The minimum spend required for delivery.
• price_range: Typically represented as $, $$, or $$$.

The Problem with Traditional Scraping

Historically, engineers approached this by using headless browsers (Playwright/Puppeteer) and writing custom regex or CSS selectors. This approach fails for three reasons:

1. Structural Fragility: A single class name change in Uber Eats' React frontend breaks your entire pipeline.
2. Anti-Bot Defense: Modern platforms use sophisticated fingerprinting that requires rotating proxies and browser header management.
3. Unstructured Output: Even if you get the HTML, you still have to write complex parsing logic to turn that HTML into clean JSON.

By using a data API like AlterLab, you skip the browser management and the parsing logic. You provide a schema; we provide the JSON.

Quick Start: Extracting Uber Eats Data

To get started, you can use the Extract API documentation to understand the endpoint-specific parameters. Below are the implementation patterns for Python and cURL.

Python Implementation

The Python client handles the heavy lifting of session management and schema validation.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

# Define the shape of the data you want
schema = {
  "type": "object",
  "properties": {
    "restaurant_name": {
      "type": "string",
      "description": "The name of the restaurant"
    },
    "cuisine": {
      "type": "string",
      "description": "The primary cuisine type"
    },
    "rating": {
      "type": "number",
      "description": "The star rating from users"
    },
    "delivery_time": {
      "type": "string",
      "description": "Estimated delivery time window"
    },
    "min_order": {
      "type": "string",
      "description": "The minimum order amount"
    }
  },
  "required": ["restaurant_name", "cuisine"]
}

result = client.extract(
    url="https://www.ubereats.com/store/example-restaurant-id",
    schema=schema,
)

print(result.data)

cURL Implementation

If you are building a service in Go, Node.js, or Rust, you can interact with the REST endpoint directly.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.ubereats.com/store/example-id",
    "schema": {
      "type": "object",
      "properties": {
        "restaurant_name": {"type": "string"},
        "rating": {"type": "number"}
      }
    }
  }'

Defining Your Schema for Precision

The power of a data API lies in the schema. Instead of generic text scraping, you are performing semantic extraction. When you define a field in your JSON schema, AlterLab's extraction engine uses LLMs to locate the data in the DOM and cast it to the correct type.

If you define rating as a number, you won't get "4.5 stars"; you will get 4.5. This allows you to immediately ingest data into databases like PostgreSQL or BigQuery without intermediate cleaning steps.

Example Output

When you call the API with the schema defined above, the response looks like this:

{
  "restaurant_name": "The Burger Joint",
  "cuisine": "American",
  "rating": 4.8,
  "delivery_time": "20-30 min",
  "min_order": "$15.00"
}

Scaling for High Volumes

If you are building a production-grade aggregator, you cannot rely on single requests. You need to manage concurrency and pagination.

For high-volume ingestion, use AlterLab's asynchronous jobs. This allows you you to fire off thousands of URLs and poll for results once they are ready, preventing your application from hanging on long-running requests.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

urls = [
    "https://ubereats.com/store/1",
    "https://ubereats.com/store/2",
    "https actually-a-list-of-hundreds-of-urls.com"
]

# Triggering asynchronous extraction for scale
job = client.extract_batch(
    urls=urls,
    schema=my_schema,
    callback_url="https://your-server.com/webhook"
)

print(f"Job ID: {job.id}")

When scaling, keep an eye on your pricing to ensure your throughput aligns with your budget. Because we use a-la-carte-style-billing, you only pay for the data you actually extract.

Key Takeaways

• Avoid Brittle Logic: Don'S write regex for dynamic-class CSS selectors. Use schema-based extraction.
• Focus on Types: Define your schema with strict types (number, string, boolean) to reduce downstream-data cleaning.
• Scale Asynchronously: Use batch jobs and webhooks for large-scale data ingestion pipelines.
• Start Simple: Follow our getting started guide to set up your environment in under 5 minutes.

If you are moving from manual scraping to an automated pipeline,- using a dedicated data API is the most-efficient way to ensure long-term reliability.

Hit reply if you have questions.