Food Delivery App Scraping API for Real-Time Data

Summary

The client is fastest growing food tech company that operates multiple cities in North America and Europe. Their core business involves real-time insights and comparing prices for several food delivery platforms like UberEats, DoorDash, Grubhub, and Deliveroo. To create a competitive-edge in the market, they required continuous access to live data including menus, prices, offers, delivery times, and store availability from these platforms.

Client Requirement

The client approached Foodspark with following requirements:

Real-time data extraction from top food delivery platforms
Scalable API access to fetch menu details, pricing, restaurant availability, promotional offers, delivery charges, and customer reviews.
Integration-ready API endpoints for internal dashboards and business intelligence services.
Continuous data refresh with minimal latency

Challenges

While addressing the requirements, our experts faced below challenges:

Anti-Scraping Measures: Platforms deployed advanced bot protection like CATPCHAs, IP rate-limiting, and dynamic content loading.

Frequent UI/DOM Changes: Food delivery platforms frequently changed their web structure, breaking traditional scrapers.

Concurrency & Volume: The requirement to scrape thousands of listings across hundreds of cities in real-time demanded huge scalability.

Geo-Fenced Content: Several platforms display different results depending on user location or delivery radius, which required simulating user behavior depending on coordinates.

Data Standardization: Menu items and pricing were inconsistent across restaurants and platforms, making data normalization a main challenge.

Solution

We developed a custom food delivery app scraping API that resolved all the challenges:

Headless Chrome-based Crawling: Enabled us to load JavaScript-rendered content and simulate real user interactions.

Geo-location Emulation: Used dynamic

Conclusion

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