We extract restaurant listings, menu hierarchies, dynamic delivery fees, surge pricing, and promotional data from GrabFood. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Restaurant Listings objects from grabfood.com. All fields typed and schema-versioned.
"restaurant_id": "4-CZE3N6A1J7X", "name": "KFC - Bugis Junction", "rating": 4.6, "review_count": 1204, "cuisine_tags": "['Fast Food', 'Western', 'Halal']", "delivery_fee_base": 3.5, "is_open": true, "estimated_delivery_time": 25
| # | restaurant_id | name | chain_name | latitude | longitude | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from grabfood.com. All fields typed and schema-versioned.
"item_id": "ITM-99214A", "restaurant_id": "4-CZE3N6A1J7X", "name": "Zinger Burger Meal", "base_price": 8.95, "currency": "SGD", "category": "Signature Meals", "is_available": true, "popular_badge": true
| # | item_id | restaurant_id | name | description | category | base_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Modifiers & Options objects from grabfood.com. All fields typed and schema-versioned.
"group_name": "Choose your drink", "option_name": "Iced Milo (Medium)", "additional_price": 1.2, "is_required": true, "min_selections": 1, "max_selections": 1, "is_available": true, "currency": "SGD"
| # | modifier_group_id | item_id | group_name | option_name | additional_price | is_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Fees objects from grabfood.com. All fields typed and schema-versioned.
"restaurant_id": "4-CZE3N6A1J7X", "scrape_coordinates": "1.2986, 103.8519", "base_delivery_fee": 3.5, "surge_fee": 2.0, "platform_fee": 0.3, "estimated_time_mins": 35, "currency": "SGD", "timestamp": "2026-05-12T11:45:00Z"
| # | restaurant_id | scrape_coordinates | timestamp | base_delivery_fee | surge_fee | small_order_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Offers objects from grabfood.com. All fields typed and schema-versioned.
"promo_id": "PRM-50OFF", "title": "50% off selected items", "discount_type": "PERCENTAGE", "discount_value": 50, "min_order_value": 15.0, "max_discount": 8.0, "valid_until": "2026-05-31T23:59:59Z"
| # | promo_id | restaurant_id | title | description | discount_type | discount_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our infrastructure manages complex coordinate spoofing, nested modifier trees, and dynamic fee rendering to deliver clean, normalised restaurant catalogues.
Capture names, coordinates, ratings, review counts, and cuisine taxonomies across entire cities.
Extract full menu hierarchies including categories, item descriptions, prices, and dietary tags.
Normalise deeply nested modifier groups, capturing required selections, max limits, and upcharge pricing.
Monitor base delivery fees, surge pricing, platform fees, and estimated delivery times from specific coordinate pairs.
Inject precise latitude and longitude coordinates to capture accurate hyperlocal availability and delivery radii.
Extract active promo codes, discount percentages, minimum order values, and validity periods.
Monitor store open/close status, scheduled operating hours, and temporary closures in real time.
Scrape data across Singapore, Malaysia, Indonesia, Thailand, Vietnam, and the Philippines using localised proxy pools.
Run hourly or daily pipelines that emit only changed records, reducing downstream storage and processing costs.
Brief in. Clean data out.
Provide target coordinates, city bounds, or specific restaurant URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, coordinate spoofing, and API interception for grabfood.com.
Schema validation, null-rate checks, price-outlier detection, and modifier tree integrity checks before launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Food delivery platforms rely on complex internal APIs and hyperlocal constraints. Here is how we maintain data integrity at scale.
GrabFood content is entirely dependent on the user's exact location. We calculate optimal coordinate grids across target cities and inject precise latitude/longitude pairs into request headers and payloads to simulate users standing in specific hex zones.
Delivery fees fluctuate based on driver supply, weather, and demand. We track these dynamic variables by maintaining continuous sessions from fixed coordinates, capturing base fees, surge increments, and small order penalties at granular intervals.
Restaurant menus contain complex decision trees (e.g., 'Choose 1 base', 'Choose up to 3 toppings', 'Add drinks'). We flatten these nested JSON structures into queryable relational formats or structured document schemas that map directly to your warehouse.
We route requests through ISP-grade residential proxies physically located in the target country (SG, MY, ID, etc.). This prevents regional blocking and ensures we receive accurate local pricing, currency, and availability data.
Instead of parsing volatile DOM elements, our Playwright instances intercept and decode GrabFood's internal GraphQL and REST API responses, yielding cleaner data, faster execution, and higher schema stability.
Restaurant chains monitor competitor menu pricing, promotional tactics, and bundle strategies across different neighbourhoods.
Operators map cuisine gaps, delivery fee heatmaps, and restaurant density to identify optimal real estate for cloud kitchens.
Competing aggregators and logistics firms track surge pricing patterns and base delivery fees to optimise their own pricing algorithms.
FMCG brands and restaurants analyse discount depths, minimum order thresholds, and campaign durations to measure promotional effectiveness.
Food analysts track trending items, category growth, and popular modifiers to advise restaurants on menu engineering.
Investors and analysts track active restaurant counts, review velocity, and geographic expansion to model platform market share.
"GrabFood dictates the food delivery market in Southeast Asia, but its hyperlocal pricing and nested menus make raw data extraction fundamentally complex."
Extracting data from GrabFood requires precise coordinate spoofing, handling complex internal API responses, and normalising massive modifier trees. DataFlirt manages this infrastructure so your data science teams can focus on market analysis rather than maintaining fragile proxy pools.
Everything supported by our grabfood.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles API interception, coordinate spoofing, and interaction flows.
We maintain pools of residential ISP proxies across Southeast Asia. Rotation happens per-request to ensure accurate regional data and prevent blocking.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About grabfood.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from GrabFood is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal user data or circumvent authentication walls.
We use coordinate spoofing. You provide a list of target latitude/longitude pairs or a bounding box, and we calculate a hex grid. Our crawlers inject these coordinates into the request headers to simulate a user at that exact location, capturing accurate local delivery fees and restaurant availability.
We support all major GrabFood markets in Southeast Asia, including Singapore, Malaysia, Indonesia, Thailand, Vietnam, and the Philippines. We route traffic through residential proxies specific to each country to ensure accurate currency and pricing.
Menus often have deeply nested options (e.g., size -> base -> add-ons). We extract these relationships and can deliver them either as nested JSON arrays or as flattened relational tables (CSV/Parquet) with parent-child IDs, depending on your warehouse schema preference.
Yes. By maintaining consistent polling from specific coordinate pairs, we can track base delivery fees versus surge increments over time, providing a time-series dataset of delivery cost fluctuations.
For continuous monitoring of specific high-value restaurants or coordinates, we can configure pipelines to run at hourly intervals. Full city-wide restaurant catalogue refreshes typically run on a daily or weekly cadence.
Absolutely. We provide a sample run targeting specific coordinates or a small list of restaurant URLs during the scoping phase, allowing you to validate the schema and modifier structures before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off restaurant catalogue extraction or a continuous delivery fee monitoring feed across Southeast Asia — we scope, build, and operate the pipeline. Tell us what you need.
