We extract restaurant catalogues, menu structures, dynamic delivery fees, and promotional data from Uber Eats. 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 Metadata objects from ubereats.com. All fields typed and schema-versioned.
"store_id": "8a9b2c3d", "name": "Burger King (Leicester Square)", "chain_name": "Burger King", "rating": 4.2, "review_count": 512, "is_open": true, "prep_time_min": 15, "delivery_fee": 1.49, "uber_one_eligible": true
| # | store_id | name | chain_name | address | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from ubereats.com. All fields typed and schema-versioned.
"item_id": "f7e6d5c4", "category": "Flame Grilled Burgers", "item_name": "Whopper Meal", "price": 8.99, "currency": "GBP", "sold_out": false, "dietary_tags": "['Halal']", "calories": 1150
| # | item_id | store_id | category | item_name | description | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Options & Customisations objects from ubereats.com. All fields typed and schema-versioned.
"modifier_group_id": "m1n2p3", "item_id": "f7e6d5c4", "group_name": "Choose your drink", "min_selection": 1, "max_selection": 1, "option_name": "Coca-Cola Zero", "option_price": 0.0, "is_default": true
| # | modifier_group_id | item_id | group_name | min_selection | max_selection | option_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Fees objects from ubereats.com. All fields typed and schema-versioned.
"store_id": "8a9b2c3d", "base_delivery_fee": 1.49, "service_fee_pct": 10.0, "small_order_fee": 2.0, "minimum_order_value": 15.0, "estimated_time_min": 20, "estimated_time_max": 35
| # | store_id | base_delivery_fee | service_fee_pct | small_order_fee | minimum_order_value | tax_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions objects from ubereats.com. All fields typed and schema-versioned.
"store_id": "8a9b2c3d", "promo_type": "BOGO", "discount_pct": 100, "min_spend": 0.0, "promo_description": "Buy 1 get 1 free on selected items", "valid_until": "2026-12-31T23:59:59Z"
| # | store_id | promo_type | discount_amount | discount_pct | min_spend | max_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Uber Eats scraper handles geolocation spoofing, complex GraphQL API interception, and aggressive bot mitigation to deliver structured restaurant data at scale.
Categories, items, prices, modifiers, and descriptions extracted hierarchically. We capture the complete menu tree for every restaurant.
Pass precise latitude and longitude coordinates to capture hyper-local delivery zones, fees, and restaurant availability.
Extract base delivery fees, service fees, and surge pricing based on time of day and specific delivery coordinates.
Identify virtual brands operating from the same physical kitchen coordinates by normalising address strings and geospatial data.
Capture complex nested menu options, mandatory selections, upcharge pricing, and default choices accurately.
Filter and extract vegan, vegetarian, gluten-free, and halal indicators per menu item directly from the platform.
Track Buy One Get One offers, Uber One exclusive discounts, and minimum spend thresholds across competing brands.
Extract ubereats.com data across US, UK, AU, and EU markets from a unified schema with localised currency handling.
Run pipelines daily or hourly to catch menu price updates and out-of-stock items without processing full re-crawls.
Brief in. Clean data out.
Provide target postcodes, coordinates, or specific store URLs. We design the extraction schema together.
We configure GraphQL interception, location spoofing headers, proxy rotation, and Datadome bypass for ubereats.com.
Schema validation, null-rate checks, location accuracy verification, and sample menus before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Food delivery platforms invest heavily in scraping detection. Here is how we stay resilient and deliver clean data.
Uber Eats requires precise coordinates to render restaurants and fees. We manage session cookies and location headers to simulate traffic from specific delivery addresses, ensuring accurate hyper-local data.
Menu data loads via complex GraphQL queries. We intercept these payloads directly, avoiding brittle DOM parsing and ensuring complete modifier extraction without data loss.
Uber Eats uses aggressive Datadome bot protection. We rotate residential proxies and spoof TLS fingerprints to maintain high success rates without triggering CAPTCHA blocks.
Virtual brands share physical addresses. We normalise coordinates and address strings to map multiple digital storefronts to single physical kitchens for accurate market analysis.
Menu payloads are massive. We hash item states and only emit records when prices, availability, or delivery fees change, reducing downstream processing load.
Track competitor menu pricing, delivery fees, and service charges across different postcodes to maintain parity.
Identify virtual restaurant saturation and operational hubs by mapping shared coordinates across multiple listings.
Analyse popular categories, modifier structures, and pricing tiers to optimise your own digital storefront conversion rates.
Map restaurant density, cuisine gaps, and delivery coverage zones to select optimal locations for new dark kitchens.
Monitor competitor discount frequency, Uber One participation, and BOGO offer effectiveness in real time.
Compare restaurant availability and pricing parity between Uber Eats, Deliveroo, and DoorDash for the same merchants.
"Uber Eats holds the definitive map of local food commerce, but extracting structured menus and dynamic fees requires sophisticated geolocation and API interception."
Food delivery platforms aggressively protect their data with Datadome and complex GraphQL structures. DataFlirt handles the proxy rotation, coordinate spoofing, and nested JSON parsing so your team can focus on market analysis instead of scraper maintenance.
Everything supported by our ubereats.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 and deduplication. Playwright manages location headers, session cookies, and GraphQL interception.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to bypass Datadome.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in Postgres.
Data delivered to where your team already works — no new tooling required.
About ubereats.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Uber Eats is generally permissible under applicable law in India, the US, and the UK. DataFlirt targets only public, non-authenticated restaurant, menu, and pricing data. We do not extract personal data or circumvent authentication walls. Clients should review Uber Eats ToS and consult legal counsel for specific use cases.
We use precise latitude and longitude coordinates to simulate users at specific delivery addresses. This ensures we capture accurate delivery fees, hyper-local restaurant availability, and exact estimated delivery times.
Yes. We intercept the GraphQL payloads directly, allowing us to capture the full nested hierarchy of add-ons, required choices, default options, and upcharges exactly as they are configured in the restaurant point-of-sale system.
Yes. We capture base delivery fees, service fee percentages, small order fees, and dynamic surge pricing based on the specific time of the crawl and the provided location coordinates.
We match physical addresses and exact geolocation coordinates across multiple restaurant listings. If five different virtual brands share the same kitchen coordinates, we flag them as operating from a single physical hub.
Pipelines can run daily for full catalogue updates or hourly for availability and surge fee monitoring on a targeted subset of restaurants. We use change detection to only push data when values change.
Yes. We provide a sample run of up to 50 restaurants in your target postcodes as part of the pre-engagement scoping process, allowing you to validate schema fit and modifier completeness before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off restaurant catalogue extraction or continuous fee monitoring across 10,000 postcodes, we build and operate the pipeline. Tell us what you need.
