Restaurant Data Scraping

Wondering about the benefits of restaurant data?

Restaurant data extraction has taken the industry by the storm. Restaurant owners are extracting competitor menu data to create innovative menus and exclusive deals that set them apart. Understanding the industry pricing for various items can help develop a competitive pricing strategy. With web scraped restaurant data get ready to explore unlimited opportunities.

Restaurant database creation using Zyte data extraction

Use restaurant data scraping to get a clean, valuable restaurant database that includes multiple data specifications such as locations, menus, reviews, mentions, and much more. All this without the technical hassle. You tell us where it is online and our team of experts gets you the data, exactly how you want it.

With our high-quality restaurant data feeds, you can get access to structured restaurant data from one or multiple websites across the web, so you can create a perfect restaurant database. As your needs grow, so can we.

Zyte Data Extraction supports a comprehensive list of metadata types and we have a QA process perfected for 10+ years to deliver high-quality restaurant web data.

Restaurant data scraping categories

• Locations
• Reviews
• Listings
• Mentions
• Opening hours
• Menu items
• Ratings
• Other custom fields upon request

Example output of web extracted restaurant database

[
  {
    "name": "The River Kitchen",
    "cuisine": "Cafe Food, Coffee and Tea",
    "locality": "Hamilton Central",
    "city": "Hamilton & Waikato",
    "country": "New Zealand",
    "num_of_reviews": "74",
    "review_rating": "4.3",
    "opening_hours": {
      "Monday": "7am – 4pm",
      "Tuesday": "7am – 4pm",
      "Wednesday": "7am – 4pm",
      "Thursday": "7am – 4pm",
      "Friday": "7am – 4pm",
      "Saturday": "8am – 4pm",
      "Sunday": "8am – 3pm"
    },
    "latitude": -37.788569974,
    "longitude": 175.284005329,
    "scrape_url": "https://www.example.com/restaurant",
    "does_delivery": "false",
    "cost_for_two": "NZ$40",
    "restaurant_type": "Café",
    "scrape_timestamp": "2020-02-17 02:50:05.708799"
  }
]