🌎 UberEats Search Scraper by Location and Keyword [RENTAL]

Discover a smarter way to access restaurant data with Uber Eats Discovery by Location and Keyword Scraper. Skip the hassle of copying URLs—just enter a search term and location to unlock real-time restaurant insights in seconds. Ideal for instant market research or competitive scouting, all in a single streamlined tool. Efficient, effortless, and designed for fast results.

✨ What does this scraper do?

• Searches for stores/restaurants on Uber Eats according to one or more locations and a search keyword.
• Extracts relevant information from each store, such as name, address, promotions, city, country, and more.
• Allows you to limit the maximum number of results per search.
• Returns the data in a structured (JSON) format, ready for analysis or integration.

🔍 How does it work?

1. Input:
   The actor receives an input with:

   • A list of addresses or locations.
   • A search keyword (e.g., "pizza", "sushi", etc.).
   • A maximum number of results per location.

2. Process:
   For each location:

   • Navigates to Uber Eats and simulates a search as a real user.
   • Extracts data from the stores matching the search term.
   • Repeats the process for all specified locations.

3. Output:

   • A JSON file containing the data of the discovered stores, including details like name, address, city, country, promotions, etc.

Example input

{
  "locations": [
    "95th Ave, Ozone Park, NY 11416, USA",
  ],
  "search_keyword": "pizza",
  "max_search_results": 20 // Each location will have a maximum of 20 results, If there is less than that fewer results will be returned
}

Example output

[
{
  "store_uuid": "35e7a890-3f2a-5948-815e-7a2a3cb77d1f",
  "name": "Pizza Near Me",
  "url": "https://www.ubereats.com/store/pizza-near-me/NeeokD8qWUiBXnoqPLd9Hw",
  "estimated_time_to_delivery": "20 min",
  "rating": 4.526315789473685,
  "rating_count": "14",
  "images": [
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/fb86662148be855d931b37d6c1e5fcbe.webp",
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/783282f6131ef2258e5bcd87c46aa87e.webp",
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/8a42ee7a692dfa4155879820804a277f.webp",
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/fdf52d66534809b650058f41d517d74a.webp",
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/9b3aae4cf90f897799a5ed357d60e09d.webp",
    "https://tb-static.uber.com/prod/image-proc/processed_images/8018381e4969565eb152608e0b9d106e/f6deb0afc24fee6f4bd31a35e6bcbd47.webp"
  ],
  "city": "Newark",
  "country_code": "US",
  "extraction_date": "2025-07-18",
  "extraction_datetime": "2025-07-18T05:44:25Z",
  "input": {
    "search_location": "85 Marsh St, Newark, NJ 07114, USA"
  }
}
]

📚 How to use this actor

1. In Apify Console:

   • Click "Run" and provide the input in JSON format as shown above.

2. From code (Python/Node.js):
   You can run the actor using the Apify SDK or by making an HTTP request to the Apify API.

🗒️ Technical notes

• The scraper uses asynchronous HTTP requests for efficiency.
• Retry mechanisms and cookie handling are implemented to simulate real user behavior.
• The code is prepared to handle pagination and large result sets.

⚠️ Limitations

• Scraping Uber Eats may be subject to changes in the website structure or anti-bot measures.
• Using proxies may be necessary for large volumes or to avoid blocking.

💬 Support

• If you need any help please contact us as soon as possible via email or any contact channel that Apify provides

Credits

Developed and maintained by ScraperUnit team 🌟.