Food and restaurant datasets turn public menus, prices, reviews, and listings into clean files you can analyze right away. However, most free files online are years old or cover a single city. So this guide lists the 10 datasets that restaurant brands, food tech teams, and FMCG companies use most. For each one, it shows the key fields, the usual sources, and the best uses. It also compares free public datasets with custom data, so you can choose the right option.

Food and Restaurant Datasets at a Glance

What Makes Food and Restaurant Datasets Ready to Use?

A ready-to-use dataset is clean, structured, and consistent, so your team can load it without fixing it first. By contrast, raw scraped data often has duplicates, mixed currencies, and broken fields. That is why good food and restaurant datasets share five traits:

• Clean fields: every record uses the same columns, units, and currency, so files load without fixes.
• No duplicates: each restaurant or product appears once, even when several apps list it.
• Location tags: every row carries a city, postcode, or coordinates, so you can compare areas.
• Timestamps: each record shows when it was collected, because prices and offers change often.
• Standard formats: files arrive as CSV or JSON, or through an API, so they fit your tools.

In addition, the refresh rate matters as much as the fields. For example, a one-time file suits a market study. However, price and offer tracking needs daily or even hourly updates.

Restaurant Datasets

The first group of food and restaurant datasets covers restaurants themselves: what exists, what it sells, and what diners think.

1. Restaurant Listings Dataset

A listings dataset records every restaurant in an area, with its name, address, cuisine, coordinates, opening hours and rating. For example, a chain can count rivals within 3 km of a planned site. It can also spot delivery-only brands that never appear on the street. Common sources include Google Maps, Yelp, and delivery apps. To learn more, see how FoodSpark supports restaurant location gap analysis.

2. Restaurant Menu Dataset

A menu dataset lists each dish with its description, price, category, and add-ons. As a result, you can compare what rivals charge for the same item, such as a large pepperoni pizza. It also shows menu size, dietary labels, and new launches over time. Meanwhile, food tech apps use menu data to fill their own catalogs. For details, see our restaurant menu data scraping service and US restaurant menu dataset guide.

3. Restaurant Reviews and Ratings Dataset

A reviews dataset collects star ratings, review text, and review dates for each restaurant. Ratings matter because they shape sales. In fact, a 2011 Harvard Business School study linked one extra Yelp star to 5% to 9% more revenue. The effect applied mainly to independent restaurants. So brands track their own ratings and their rivals’ ratings side by side. They also group review text into themes, such as late delivery or cold food. To learn more, see our restaurant reviews and ratings data scraping service and review API.

Food Delivery Datasets

The next three food and restaurant datasets come from delivery apps, where prices, fees, and offers change fastest.

4. Delivery Menu Prices Dataset

Menu prices on delivery apps can differ from in-store prices. A delivery menu prices dataset captures the price of each dish on each app, by area. For instance, it shows whether a burger costs more on Uber Eats than on DoorDash. It also reveals the gap between app and in-store menus. As a result, brands can check their channel pricing and spot rivals’ markups. To learn more, see our food aggregator scraping and competitor price monitoring services.

5. Offers, Fees and Delivery Times Dataset

The menu price is only part of what diners pay. So this dataset tracks discounts, delivery fees, service fees, minimum orders, and delivery time estimates. For example, DoorDash says its service fee varies with distance and order size. In addition, offers can change by area and by hour. That is why this dataset works best with daily or hourly updates.

6. Swiggy and Zomato Dataset

India’s food delivery market runs mainly on two apps. By late 2025, Zomato held about 57.3% of food delivery order value, while Swiggy held about 42.7%, according to INDmoney. On top of that, Swiggy averaged about 265,800 monthly transacting restaurant partners from April to June 2026. A combined dataset usually includes menus, prices, offers, ratings, cuisines, and delivery times by city. For details, see our Swiggy API and Zomato API.

Grocery and Quick Commerce Datasets

The last four food and restaurant datasets cover grocery and quick commerce. Here, prices and stock can change by store, by pin code, and even by hour.

7. Grocery Product and Price Dataset

This dataset lists each product with its name, brand, pack size, price, promotion and category. For example, a consumer goods brand can compare its shelf price on Instacart, Walmart, and Kroger in one ZIP code. Similarly, retailers use it to benchmark their own prices against rivals. To learn more, see our grocery data scraping API and Instacart API.

8. Quick Commerce Dataset by Pin Code

Quick commerce apps deliver from small dark stores, so prices and stock can differ by area. In India, Blinkit had 2,443 stores at the end of June 2026. Meanwhile, Swiggy Instamart had 1,171 dark stores in 131 cities. As a result, FMCG brands need data by pin code, not just by city. This dataset usually covers selling price, MRP, discounts, stock status, and search rank. See our quick commerce data scraping service and Blinkit API.

9. Stock Availability Dataset

When a product is out of stock, the sale often goes to a rival. A stock availability dataset records whether each product is in stock, by store or delivery area, at each check. For instance, a brand can see which dark stores ran out of its best seller last weekend. In turn, its sales team can raise those gaps with each retailer. Because stock moves fast, this dataset works best with daily or hourly checks.

10. Liquor and Alcohol Price Dataset

Alcohol prices vary by store, state, and country, partly because of local taxes and rules. A liquor dataset covers brands, bottle sizes, prices, promotions, and stock by store. For example, a wine brand can track its price across retailers in each US state. To learn more, see our liquor store data scraping service and US alcohol price data guide.

Free Food and Restaurant Datasets vs Custom Data

Free food and restaurant datasets are a good place to start, especially for learning and testing. Here are four well-known options:

When Free Food and Restaurant Datasets Fall Short

Free files work well for research, models, and student projects. However, check each license before any commercial use. For instance, Yelp says its dataset is intended for educational use. In addition, coverage is often narrow. The Zomato file, for example, covers one city at one point in time. Similarly, Yelp’s file spans 11 metro areas, so most cities are missing.

On top of that, free files rarely include delivery prices, fees, offers, or stock levels. So businesses that track live markets usually need custom data. In that case, a provider collects the exact apps, cities, and fields you need. Then it refreshes them on a set schedule.

How to Choose Food and Restaurant Datasets

Before you buy or build food and restaurant datasets, ask six questions:

1. Coverage: does it include every app, city, and store you compete in?
2. Fields: does it carry the exact columns your team needs, such as MRP and selling price?
3. Freshness: how often is it refreshed, and does each row show when it was collected?
4. Location detail: can you filter by city, postcode, pin code, or radius?
5. Format: will it arrive as CSV, JSON, or an API that your tools can read?
6. Compliance: does the provider collect only public data and avoid personal details?

Then ask for a sample before you commit. After all, a small sample shows data quality faster than any sales call.

How FoodSpark Delivers Food and Restaurant Datasets

FoodSpark collects public data from delivery apps, restaurant sites, and grocery platforms. For example, its pricing page lists DoorDash, Uber Eats, Grubhub, Instacart, Swiggy, Zomato, Blinkit, Zepto, and Walmart. Then it cleans, deduplicates, and standardizes each file before delivery.

FoodSpark also says it does not collect personal or identity-related information. To compare options, see our food data API pricing page.

Who Uses Food and Restaurant Datasets?

Different teams use food and restaurant datasets for different jobs. Here is how the main buyers use them:

In short, the right dataset depends on the decision you need to make. For example, site selection needs listings and ratings, while a pricing review needs menus, fees, and offers. So start from the question, and then pick the fields. For instance, see how Swiggy datasets map cuisine gaps for cloud kitchens. You can also explore more food delivery analytics use cases.

Food and Restaurant Datasets: FAQs

What are food and restaurant datasets?

Food and restaurant datasets are structured files of food business data, such as listings, menus, prices, reviews, and grocery products. Most come from public apps and websites.

Where can I find free food and restaurant datasets?

Good free options include the Yelp Open Dataset, Open Food Facts, and USDA FoodData Central. However, check each license before any commercial use.

Is there a Swiggy or Zomato dataset?

Yes. A popular free Zomato file on Kaggle has about 51,717 Bangalore restaurant rows. For current data across cities, though, you need a custom dataset or an API.

How often should a food dataset be updated?

It depends on the use. For example, a market study can use a one-time file, while price and offer tracking needs daily or hourly updates.

What format do food datasets come in?

Most come as CSV or JSON files. In addition, many providers offer an API for live data.

How do I get a free dataset sample?

First, tell us the apps, cities, and fields you need. Then FoodSpark sends a free 100-row sample in CSV or JSON.