```yaml
product: AlterLab
title: Uber Eats Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-06-28
canonical_facts:
- "Learn how to build a robust uber eats data api pipeline to extract structured JSON like restaurant names, ratings, and cuisine types using schema-based extraction."
source_url: https://alterlab.io/blog/uber-eats-data-api-extract-structured-json-in-2026
```
# 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](/docs/api/extract) 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.
```python title="extract_ubereats_com.py" {5-12}
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.
```bash title="Terminal"
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"}
}
}
}'
```
- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output
## 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:
```json title="response.json"
{
"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.
```python title="batch_extraction.py" {3-8}
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](/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.
1. **Define Schema** —
2. **** —
3. **Receive Typed JSON** —
## 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](/docs/quickstart/installation) 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.
## Frequently Asked Questions
### Is there an official Uber Eats data API?
Uber Eats does not provide a public API for bulk restaurant data. AlterLab provides a data API solution that extracts publicly available information into structured JSON formats.
### What Uber Eats data can I extract with AlterLab?
You can extract any publicly visible information, such as restaurant names, cuisine types, ratings, delivery times, and minimum order requirements.
### How much does Uber Eats data extraction cost?
AlterLab uses a pay-as-you-go model. You only pay for the extractions you run, with no monthly minimums, making it ideal for both small projects and enterprise pipelines.
## Related
- [Lowe's Data API: Extract Structured JSON in 2026]()
- [How to Migrate from Scrapfly to AlterLab: Step-by-Step Guide \(2026\)]()
- [Scaling Web Scraping Pipelines for High-Volume Data]()