DATA SOLUTION
Build Structured Restaurant & Food Data.
Build structured restaurant, menu, pricing, availability, location, and review datasets.
THE CHALLENGE
Getting reliable restaurant data from the web is harder than it looks.
Fragmented platforms
Restaurant and menu data is spread across delivery apps, restaurant websites, aggregators, and review platforms, each with its own structure.
Location-specific menus
Prices, availability, and menus often differ by city, locality, or outlet, requiring location-aware collection.
Menu complexity
Menus include categories, items, modifiers, combos, portion sizes, and pricing that vary widely across cuisines.
Constant menu changes
Prices, offers, item availability, and delivery times change frequently across platforms.
Review and rating noise
Ratings and reviews vary across platforms, requiring normalization before analysis.
Duplicate outlets
The same restaurant appears under different listings across platforms, requiring deduplication logic.
THE MYDATASCRAPER APPROACH
From fragmented web pages to structured business intelligence.
MyDataScraper collects restaurant and menu information from the sources you select, then structures, validates, and normalizes it into one consistent dataset — with cuisine mapping, location structuring, menu hierarchy handling, and historical tracking built in — so your product, marketing, and analytics teams work with comparable records instead of raw pages from dozens of delivery platforms and restaurant sites.
What you can build with Restaurant & Food Data.
Restaurant listings
Collect structured outlet information across delivery platforms and restaurant websites.
Menus
Extract menu categories, items, descriptions, modifiers, and portion sizes.
Prices
Capture item prices, packaging charges, delivery fees, and applicable taxes where exposed.
Offers & discounts
Track combos, discounts, promotions, and platform-specific offers.
Availability
Monitor item availability, delivery time estimates, and service areas where exposed.
Locations
Structure addresses, localities, cities, and coordinates into geospatially usable fields.
Ratings & reviews
Collect review counts, average ratings, and top review snippets.
Cuisine tagging
Normalize cuisine and category tags into a unified taxonomy.
Historical tracking
Accumulate menu and pricing history for trend and demand analysis.
Multi-city coverage
Collect data across multiple cities and localities in a single unified dataset.
Build the dataset around your required fields.
| FIELD | EXAMPLE |
|---|
Available fields depend on the source website and specific use case.
Collect data from the sources your business depends on.
DELIVERY PLATFORMS
- Food delivery apps
- Quick commerce platforms
- Cloud kitchen listings
RESTAURANT WEBSITES
- Brand websites
- Outlet pages
- Menu pages
AGGREGATORS
- Restaurant directories
- Food discovery platforms
- Local listings
REVIEW PLATFORMS
- Review websites
- Consumer forums
- Rating aggregators
Sources can be evaluated based on your target data and collection requirements. Not every website can be supported.
A data workflow designed around your requirements.
-
01
Define
Identify sources, fields, locations, filters, and update frequency.
-
02
Extract
Collect relevant information from the selected sources.
-
03
Structure
Normalize fields and standardize records.
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04
Validate
Check records, remove duplicates, and apply quality controls.
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05
Deliver
Send data through the selected delivery method.
Receive your data the way your systems need it.
How frequently can the data be updated?
One-Time
A single dataset delivered once.
Daily
Refreshed every day.
Weekly
Refreshed every week.
Scheduled
Custom intervals you define.
Near Real-Time*
Where the source and workflow allow.
*Update frequency depends on source availability, technical requirements, and the specific workflow.
What businesses use this data for.
Food discovery
Power food search, discovery, and recommendation products.
Pricing benchmarks
Compare menu pricing across brands, cuisines, and cities.
Outlet tracking
Monitor restaurant expansion and new outlet launches.
Menu intelligence
Track menu changes, new items, and seasonal offers.
Offer monitoring
Track discounts and combos across delivery platforms.
Review intelligence
Analyze ratings and sentiment across outlets and cuisines.
Location analytics
Feed location planning and demand forecasting models.
Reporting & dashboards
Power internal BI dashboards with refreshable restaurant data.
Built for teams across data-intensive industries.
API DELIVERY
Put restaurant & food data directly into your application.
Access structured data through an API instead of maintaining extraction infrastructure yourself.
Illustrative example only. Endpoints and fields are defined per project.
GET /api/restaurants? city=mumbai cuisine=north-indian
{
"name": "Example Kitchen",
"brand": "Example Foods",
"cuisine": "North Indian, Mughlai",
"locality": "Example Area",
"city": "Mumbai",
"rating": 4.3,
"review_count": 1540,
"price_range": "\u20b9199 \u2013 \u20b9499",
"delivery_time_minutes": 35,
"availability": "available",
"source": "delivery_platform_a",
"captured_at": "2026-10-02T09:00:00Z"
}
See the data before you build around it.
Review a sample dataset to understand the structure, fields, and level of detail available for your use case.
| RESTAURANT | CUISINE | CITY | PRICE RANGE | RATING | AVAILABILITY |
|---|---|---|---|---|---|
| Example Kitchen | North Indian | Mumbai | ₹199 – ₹499 | 4.3 | Available |
| Urban Bowl | Asian | Bengaluru | ₹249 – ₹599 | 4.5 | Available |
| Pasta Point | Italian | Pune | ₹299 – ₹699 | 4.2 | Available |
| Tandoor House | North Indian | Delhi | ₹199 – ₹549 | 4.4 | Available |
| Green Leaf Cafe | Vegetarian | Chennai | ₹149 – ₹399 | 4.1 | Limited |
More than extraction.
Data engineering
Design workflows around your actual data requirements.
Structured output
Receive consistent and analysis-ready datasets.
Custom workflows
Choose sources, fields, frequency, and delivery around your use case.
Ongoing data operations
Support recurring workflows as source structures and requirements evolve.
Frequently asked questions.
What restaurant data can be collected?
Restaurant name, cuisine, address, location, opening hours, menu items, prices, discounts, delivery fees, availability, ratings, reviews, and source URLs — depending on what each source exposes.
Which platforms can you collect from?
Food delivery platforms, restaurant websites, cloud kitchen listings, aggregators, and review sites. Share the sources you have in mind and we will assess feasibility.
Can data be scoped by city or locality?
Yes. Datasets can be scoped by city, locality, cuisine, brand, or outlet type depending on the source.
How do you handle location-specific menus?
Menus, prices, and availability are collected with location context where the source supports it, so records reflect the correct city or outlet.
Can you collect full menu items and categories?
Yes. Menu categories, items, descriptions, modifiers, and portion sizes can be structured into a hierarchical dataset.
Can I track prices and offers over time?
Yes. Recurring workflows build a historical dataset of prices, offers, and menu changes across outlets.
Can you collect ratings and reviews?
Yes. Ratings, review counts, and review snippets can be collected where the source exposes them.
What output formats are available?
JSON, CSV, Excel, database delivery, scheduled feeds, cloud storage, or a custom integration.
Can I receive the data as an API?
Yes. Data can be delivered through a REST API designed around your search, pricing, or dashboard needs.
How often can the data be updated?
Workflows can run daily, weekly, monthly, or on custom schedules depending on the source and your requirements.
Can you provide a sample dataset?
Yes. Request a sample to review the structure, fields, and level of detail for your use case.
How do you handle changes on food platforms?
Recurring workflows are maintained and adjusted as source structures change so delivery keeps matching your requirements.
Build the Restaurant & Food Data workflow your business needs.
Tell us what data you need, which sources matter, how often it should be updated, and how you want to receive it.
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