Building An India Food Delivery Dataset: Swiggy, Zomato & Ondc

By Author: Actowiz Solution
Total Articles: 81
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Introduction
India's food-delivery market runs on Swiggy and Zomato, with ONDC opening a new open-network dimension — and the data flowing through them is a real-time map of how India eats out: what restaurants charge, how delivery menus differ from dine-in, which items sell where, how fees vary by locality, and the explosion of cloud kitchens reshaping supply. For restaurant chains, cloud-kitchen operators, CPG brands, and analysts, a structured food-delivery dataset is one of the highest-signal assets in the market — but building one correctly is far harder than it looks.
Step 1: Define the Unit — Restaurant, Menu, or Item?
The first decision shapes everything. Restaurant-level (coverage and presence), menu-level (full menus and structure), or item-level (the dish, its price, customisations, availability). Most serious datasets are built item-level with restaurant and menu context preserved, because pricing, promotion, and demand analysis all happen at the item level.
Worked example — the biryani that costs three prices. A chain assumed its signature biryani was priced consistently. Item-level ...
... data across Swiggy and Zomato in one city showed three different delivery prices for the same item at the same outlet — platform-commission-driven divergence it had never seen, because its old view stopped at "the restaurant is listed."
Step 2: Capture the Fields That Actually Matter
A useful item-level record carries far more than "dish name and price": item identity, the delivery price (which differs from dine-in and across platforms), customisations and combos, item-level availability, the full fee stack (delivery fee, platform fee, surge, packaging, GST), promotions and coupons distinguished from base price, ratings, delivery time, and locality context. The effective price and fee structure matter as much as the menu price — a dataset capturing only menu prices misses how the customer actually experiences cost.
Worked example — the invisible fee stack. A cloud kitchen benchmarked its bowl at "₹249, same as the rival." After delivery fee, platform fee, packaging, and surge, the effective cost to the customer was ₹340 on one platform and ₹385 on another — a gap quietly sending price-sensitive orders elsewhere. Menu price said "matched"; effective price said "losing the basket."
Step 3: Handle the Locality Problem
Indian food delivery is hyperlocal — the same restaurant shows different availability, fees, delivery times, and sometimes prices depending on the delivery locality. A dataset from one locality describes one neighbourhood, not a market. The fix is a locality panel — a deliberate set of delivery locations chosen to represent the markets you care about, queried every cycle.
Worked example — the sold-out suburb. A CPG brand tracking its dessert range on delivery apps pulled data from one central location and saw healthy availability. A locality panel across the metro revealed its product showing unavailable across an entire suburban cluster — a fulfilment gap invisible from the centre, costing the highest-AOV neighbourhood.
Step 4: Map the Cloud-Kitchen Explosion
India's defining food-delivery dynamic is the cloud-kitchen boom — virtual brands multiplying, often several operating from one physical kitchen, appearing and vanishing fast. A dataset that treats only "real" restaurants misses a huge and fast-growing share of supply. Detecting virtual brands, mapping brand-to-kitchen relationships where visible, and catching new-brand launches is uniquely important in India.
Worked example — five brands, one kitchen. A market analyst discovered that five distinct "brands" on the apps operated from a single cloud kitchen — a supply-concentration insight completely invisible at the brand level, and exactly the kind of dynamic reshaping the Indian market.
Step 5: Get the Timing Right
Food delivery moves through the day — restaurants open and close, items sell out, fees surge at meal peaks and in monsoon, promotions launch and expire. Multiple daily collection windows with consistent scheduling capture the intraday rhythm — a lunch reading compared to a lunch reading.
Step 6: The Hard Part (Where Actowiz Comes In)
Swiggy and Zomato are app-first, defended, dynamic platforms; ONDC adds a new open-network structure. Reliable, recurring, complete extraction across them at the right cadence — with cross-platform restaurant/item matching and cloud-kitchen detection — requires self-healing infrastructure and real engineering, delivered compliantly (public data only, DPDP-mapped). This is Actowiz Solutions' home market.
How Actowiz Solutions Delivers
Actowiz builds India food-delivery datasets across Swiggy, Zomato, and ONDC — item-level, locality-panelled, multi-window, cross-platform-matched, cloud-kitchen-aware, on self-healing infrastructure, delivered clean and recurring.
Frequently Asked Questions
What's the right grain?
Item-level with restaurant and menu context — pricing, promotion, and demand analysis all happen there.
Why does locality matter?
Food delivery is hyperlocal; a single-locality dataset describes one neighbourhood, a locality panel makes it representative.
Why is cloud-kitchen mapping important in India?
Because virtual brands are a huge, fast-growing, fast-churning share of supply — and multiple brands often run from one kitchen, a dynamic invisible at the brand level.
Can I see it first?
Yes — request a live scraping demo from Actowiz Solutions.
Conclusion
You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!