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Restaurant Menus

Info

This dataset is available for any market, samples are listed for similar countries as Unified Restaurant Establishments

Dotlas maintains a unified, structured collection of restaurant menus aggregated from multiple online and offline data sources. This dataset brings together dish-level and section-level information for restaurants, normalized across platforms like delivery apps, brand websites, menu scans, and proprietary field audits.

Each menu is linked to a verified restaurant entity in the Unified Restaurant Establishments dataset and enriched with standardized food categories, inferred product names, and pricing information at both the individual source level and a computed average price across sources.

The result is a powerful, analysis-ready dataset that enables clean comparisons of pricing, product assortment, and section structure across restaurants, brands, and neighborhoods.

A ridge-plot showing variation in pricing of complete menu for various coffee brands in the UAE

Data Dictionary

All menu rows are linked to a restaurant and follow a consistent schema, filterable by restaurant_id, item_name, or other fields

Column Name Data Type Description
restaurant_id string Unique Dotlas-assigned identifier for the restaurant
brand_id string Unique Dotlas-assigned identifier for the chain / brand of the restaurant
item_name string Name of the dish or menu item
item_description string Free-text description of the dish as seen on the source menu
section_name string The section of the menu the item belongs to (e.g. Appetizers, Main Course)
section_description string Any description available for the section as a subtitle in the menus
menu_name string Lunch or Dinner Menu / Drinks Menu or A La Carte, etc.
menu_description string A description of the menu if available
avg_price float Average price of the item across multiple observed sources

Tip

This is a snapshot dataset, which means menus can be captured on a periodic basis for markets on a subscription to measure price changes, new or retired items and more over time. This would involve an additional timestamp field to see menus as a time-series.

Additional Menu Fields Available on Request

  • Product Information & Categorization

    Use our enhanced attributes which detail the product name and product category with sizing. This data is especially useful for comparing menu items by product in the market and judging average cost for a basket of items. Additional hierarchical categorization can also be made available for more intricate analyses.

    Column Name Data Type Description
    product_name string Standardized product name inferred across menu items (e.g. "chicken wings")
    item_category string Higher-level food or beverage category (e.g. "beer", "burger", "dessert")
    product_sizing string Parsed serving size (e.g. 330ml, 12 pieces, 1 bottle, Large)

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  • Stock-Keeping Item Attributes

    Information regarding an item's availability by judging if still in stock for purchase, or if specific discounts or promotions are available for items with a strikethrough price shown to customers, along with other metadata.

    Column Name Data Type Description
    is_available boolean Whether or not the item is out of stock
    is_popular boolean Whether the item is marked as popular by some sources or if it appears in suggestive section names like "Most Trending" or "Best Selling"
    item_price_discounted string Available alongside item_price per measurement of restaurant menu to judge if certain items are available with an offer or promotion

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If you're looking for a comprehensive changelog of menu events over time for a market, our Menu Events data product provides a real-time feed of menu modifications across restaurants. This specialized dataset tracks market movements in pricing, product availability, and menu composition on a high-frequency basis, maintaining a detailed record of all changes observed over time.

The data stream can be:

  • Consumed via API endpoints for real-time monitoring
  • Integrated into notification systems to alert on specific changes
  • Used to track competitor pricing and product strategies
  • Monitor stock availability and inventory patterns
  • Analyze seasonal menu rotations and pricing trends

This enables use cases from automated competitive intelligence to supply chain optimization. The feed format makes it easy to build applications that react to market changes as they happen. Example visualization below.