Uber Eats Dataset
Live and Historical Data Across Uber Eats’ Global Restaurant Delivery Network
Uber Eats inherited something distinctive from its parent rideshare business: delivery fees that move with real-time demand the same way a ride fare does, rather than sitting at a fixed rate per restaurant. Order from a busy area during a Friday-night rush and the delivery fee on an identical order can be noticeably higher than it was an hour earlier — the same dynamic-pricing logic Uber built for rides, applied to food. Layer on Uber One, a membership that spans both rides and Eats under a single subscription rather than a food-delivery-only loyalty tier, and a restaurant’s effective price to a given customer depends on variables a simple menu scrape will never capture.
Tracking that properly means treating dynamic delivery-fee behavior as a core, trackable dimension of the dataset — not an occasional anomaly to explain away. That’s exactly what our dedicated Food Delivery Data Scraping pipeline is built to handle for Uber Eats specifically — an Uber Eats Food Delivery Dataset that ties menu pricing, ratings, delivery-time data, and customer reviews together across every market it covers, refreshed on a schedule you control. It sits inside our broader Food Delivery Dataset catalog, built on the same infrastructure as our Food Delivery Data Scraping services, with custom builds available through our Web Scraping Services team.
Request Free Sample DatasetQuick Stats
Price From
$199Starting Price
Records
3.4M+Total Records
Format
CSV / JSONDelivery Format
Delivery
ImmediateAvailability
18+
Cuisine Categories
2+ Yrs
Historical Depth
Global
Multi-Country Coverage
Daily
Refresh Cycle
Why Teams Choose Our Uber Eats Dataset Over a DIY Crawl
Delivery fees on Uber Eats fluctuate with demand in a way that resembles ride-fare surge pricing more than a fixed grocery delivery charge. A scraper that captures pricing once a day will miss most of that variation entirely.
One Schema, Every Market
Restaurant metadata, menu items, prices, ratings, and delivery-time estimates all arrive in a single Uber Eats dataset — normalized across every country and currency it operates in.
Item-Level Menu Precision
Our Uber Eats Menu Dataset tracks individual dish pricing and availability, not just a restaurant-level price band, so category-level food-cost comparisons are actually possible.
Scoped to What You Actually Need
Pull specific cities, countries, or the full restaurant catalog. Refresh daily, weekly, or near-live. Delivered as CSV, JSON, Excel, or a direct API feed.
Formats Your Tools Already Read
CSV and Excel exports work straight in Power BI or Tableau. JSON and API feeds ship with a documented schema for Snowflake, BigQuery, or Redshift.
Live Data, Plus Real History
See today’s menu prices and delivery times alongside 2+ years of historical rating and pricing trends across restaurants and markets.
Built for Multi-Country Comparison
Coverage spans multiple countries with currency-normalized pricing, so cross-market benchmarking doesn’t require manual conversion.
What’s Actually Inside the Uber Eats Dataset
The Uber Eats dataset answers five practical questions: what a restaurant serves, what it costs, how it’s rated, how fast it delivers, and what customers actually say about the experience. Those five questions map to the five sub-datasets described below, all keyed together on a common restaurant and item identifier across markets.
Most clients start with delivery-fee behavior, usually chasing a specific question — how much does a restaurant’s effective delivery cost swing between off-peak and peak-demand windows, or how does Uber Eats pricing compare against a competing platform for the same restaurant in the same city? Brand and franchise teams lean on the ratings layer to catch a quality dip early across multiple countries at once. Data teams mostly just want clean, timestamped records instead of raw page data pulled separately per market, which is really the core value of buying a dataset instead of building several country-specific scrapers. This dataset shares its collection standards with our broader Food Delivery Dataset catalog.
What people actually build with Uber Eats Food Delivery Dataset:
- Menu-pricing benchmarking: Compare item-level pricing for a given cuisine or dish type across competing restaurants and countries.
- Dynamic delivery-fee analysis: Build a historical record of how delivery fees fluctuate with time of day, day of week, and demand.
- Cross-country market research: Understand how menu pricing and delivery expectations differ between Uber Eats markets.
- Uber One impact tracking: Measure how membership-linked benefits affect effective delivery cost across restaurants.
- Sentiment research: Mine the Uber Eats Customer Reviews Dataset for recurring complaints about food quality, packaging, or delivery timing.
Key Metrics
Price
$199.00Format
CSV / JSON / APIRecords
3.4M+ Verified RecordsCoverage
Multiple CountriesUpdate Frequency
DailyAvailability
Instant AccessDelivery Time
ImmediatelyPreview actual dataset structure before purchase.
A Sample of the Uber Eats Dataset
Below is a small, illustrative slice of a single extract. Each row represents one menu-item observation with commercial and rating attributes captured at a point in time. Over 45 additional fields are available, including surge-fee multipliers, cuisine tags, and full price-history timestamps.
| Store ID | Store Name | Store Image 1 | Rating | Max Rating | Reviews Count | Delivery Time | Status | Available Time | Store URL | Store Image 2 |
|---|---|---|---|---|---|---|---|---|---|---|
| WcqNS30EXWyckUC2ZqxbKA | Kwik Shop | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.3 | 5.0 | 100+ | 15 min | Open | N/A | https://www.ubereats.com/store/kwik-shop/WcqNS30EXWyckUC2ZqxbKA | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvMDcwN2Y1MjkzOGU3YWZmNWEwZGJjOWZkNjVmZTBlYWIvYzY3ZDU1OGM2MzMyODFmOWM3MWZjNmE5MGVmNDcwYWUuanBlZw== |
| RX4fB7pRQJeAtf24JcNVMA | Walgreens | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.5 | 5.0 | 700+ | 28 min | Open | N/A | https://www.ubereats.com/store/walgreens-3333-e-central-ave/RX4fB7pRQJeAtf24JcNVMA | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvMGY5ZTYxMDZkOWI1ZDNhZWRiOTczMDhmNTU1NTU5MjkvYzY3ZDU1OGM2MzMyODFmOWM3MWZjNmE5MGVmNDcwYWUuanBlZw== |
| bWv1S3tQU4KHjF90zfG6Ig | DICK'S Sporting Goods | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.8 | 5.0 | 10 | N/A | Closed | 9:00 AM | https://www.ubereats.com/store/dicks-sporting-goods-2057-n-rock-road-ste-103/bWv1S3tQU4KHjF90zfG6Ig | https://d4p17acsd5wyj.cloudfront.net/eatsfeed/other_icons/restaurant_closed.png |
| Cm7TARNMWFK6wUDXTiVvQw | Petco | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.9 | 5.0 | 20 | N/A | Closed | 9:00 AM | https://www.ubereats.com/store/petco-3050-n-rock-rd/Cm7TARNMWFK6wUDXTiVvQw | https://d4p17acsd5wyj.cloudfront.net/eatsfeed/other_icons/restaurant_closed.png |
| YAU2lQfGVeGufAHtVIdo4g | The Road Runner Mexican Fast Food | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.4 | 5.0 | 3,000+ | 20 min | Open | N/A | https://www.ubereats.com/store/the-road-runner-mexican-fast-food-wichita/YAU2lQfGVeGufAHtVIdo4g | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvZTk3MjUxYjE3MTExNmQ1ZGRlODM3NDZiNDcxYTM3ZTIvOWIzYWFlNGNmOTBmODk3Nzk5YTVlZDM1N2Q2MGUwOWQud2VicA== |
| Vlucow9sQW63VjrOs8zQeQ | Regal | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.3 | 5.0 | 29 | N/A | Closed | 10:00 AM | https://www.ubereats.com/store/regal-warren-east/Vlucow9sQW63VjrOs8zQeQ | https://d4p17acsd5wyj.cloudfront.net/eatsfeed/other_icons/restaurant_closed.png |
| XOzCIMEUQ9eO-xq-aVQgUw | Burger King | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.2 | 5.0 | 900+ | 15 min | Open | N/A | https://www.ubereats.com/store/burger-king-1104-north-broadway/XOzCIMEUQ9eO-xq-aVQgUw | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvZDk2MDZmNjU3MTNkY2IwMzU4MDBlNTRhZWZlZjVhMzQvZDY5OTA4NzYwNjQwZTYxMzNiNDM3NTMyMWRkMjU5YjkuanBlZw== |
| rGsE2KeaRAapQao-IM8C9A | McDonald's® | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 4.1 | 5.0 | 1,500+ | 16 min | Open | N/A | https://www.ubereats.com/store/mcdonalds-545-n-hillside/rGsE2KeaRAapQao-IM8C9A | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvNDAzMzQwNTBmMmFlMzNjYTllNDMwODE3MTVhMWI1YzAvZDY5OTA4NzYwNjQwZTYxMzNiNDM3NTMyMWRkMjU5YjkuanBlZw== |
| grAKyFLPWIym6Vxle2nXSQ | Nathan's Famous | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 3.5 | 5.0 | 41 | 27 min | Open | N/A | https://www.ubereats.com/store/nathans-famous-4024-e-harry-st/grAKyFLPWIym6Vxle2nXSQ | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvZDU0MGE4ZGNhYjQzMDBkMzhlYWFhODI4ODM3MDY5ODUvMTEzMGNkNDI4ZWU5MjhkMzUxZDI2ZWZkYTExNzhiMjAuanBlZw== |
| OouW0aMlS-uRksgWyOwhTQ | The Burger Den | https://www.ubereats.com/search?eventSource=textV2&pl=JTdCJTIyYWRkcmVzcyUyMiUzQSUyMjIyNzElMjBOJTIwTmV3JTIwWW9yayUyMFN0JTIyJTJDJTIycmVmZXJlbmNlJTIyJTNBJTIyMTdlNTQxNDMtMGRkOS0zMmI0LTExMmMtM2FlMWVlMDJmMTU4JTIyJTJDJTIycmVmZXJlbmNlVHlwZSUyMiUzQSUyMnViZXJfcGxhY2VzJTIyJTJDJTIybGF0aXR1ZGUlMjIlM0EzNy43MjM4MDU2JTJDJTIybG9uZ2l0dWRlJTIyJTNBLTk3LjMxOTM0OTclN0Q%3D&q=fast%20food&sc=SEARCH_SUGGESTION&searchEntered=fast&searchType=GLOBAL_SEARCH&vertical=ALL | 3.7 | 5.0 | 500+ | 29 min | Open | N/A | https://www.ubereats.com/store/the-burger-den-4024-e-harry-st/OouW0aMlS-uRksgWyOwhTQ | https://cn-geo1.uber.com/image-proc/resize/eats/format=webp/width=550/height=440/quality=70/srcb64=aHR0cHM6Ly90Yi1zdGF0aWMudWJlci5jb20vcHJvZC9pbWFnZS1wcm9jL3Byb2Nlc3NlZF9pbWFnZXMvNTZkNTk4ZmQzNDE2ZGJlMDgzZjg2MmY3MjE4MzRmMTkvNjNkMTg3NDU4OTJjMTAwYmU5ZTRlZjNjNTYwYzkyMDQuanBlZw== |
The Five Layers of the Uber Eats Dataset
Rather than one flat export, the Uber Eats dataset splits into five connected layers, licensed individually or together as a complete Uber Eats Food Delivery Dataset, joined on a common restaurant and item key.
Uber Eats Menu Dataset
The descriptive layer behind every dish a restaurant lists — what it is, how it’s categorized, and how it’s presented to customers browsing the app.
- Menu item name, category (starters, mains, desserts, beverages), and cuisine tags
- Description text, portion size, and dietary flags where available
- Item image URLs where available
- Combo and meal-deal configuration where applicable
Uber Eats Price Dataset
Pricing on Uber Eats carries a layer most food-delivery platforms don’t have at the same scale — delivery fees that move with real-time demand rather than sitting at a fixed rate, closely resembling the dynamic pricing logic behind Uber’s core rideshare business.
- Item-level menu price, base delivery fee, and demand-adjusted delivery-fee data by restaurant and time window
- Full price-change history with timestamps
- Uber One discount and promotional-offer badges tied to their active window
- Service-fee and small-order-fee data where separately itemized
Uber Eats Ratings Dataset
Restaurant reputation on Uber Eats reflects the delivery experience directly, tracked at a scale that spans dozens of countries and currencies.
- Overall restaurant rating and rating-count data for statistical confidence
- Rating-distribution data by star level
- Historical rating trends to detect quality shifts over time
- Category-level rating benchmarks by cuisine and market
Uber Eats Delivery Dataset
Because Uber Eats shares courier infrastructure with Uber’s rideshare network in many markets, delivery-time performance can vary with local ride demand as much as with kitchen speed.
- Average and real-time delivery-time estimates by restaurant and area
- Delivery-radius and serviceable-area data
- Order-acceptance and preparation-time signals where available
- Historical delivery-time trends by city and time of day
Uber Eats Customer Reviews Dataset
Star ratings tell you something went wrong; written reviews tell you what. This layer keeps the detail instead of collapsing everything into an average.
- Individual reviews with rating, review text, and submission date
- Verified-order flag and anonymized reviewer identifier
- Aspect-level sentiment: food quality, packaging, delivery timing, and value for money
- Review-velocity trends useful for catching a quality issue before it shows up in order volume
Where the Uber Eats Dataset Runs Deepest
Uber Eats operates across a genuinely global footprint, and pricing behavior, delivery-fee dynamics, and cuisine mix all differ meaningfully by country.
United States
Deepest historical depth and highest restaurant density across major metros.
United Kingdom
Strong urban coverage with distinct delivery-fee dynamics versus the US market.
Canada
Established presence across major cities with consistent cuisine-category breadth.
Australia
Growing restaurant network with distinct regional pricing patterns.
Add-Ons
demand-surge delivery-fee tracking, Uber One pricing differentials, and holiday-season demand archives by country.
How Different Teams Put This Dataset to Work
The same underlying dataset ends up solving fairly different problems depending on who’s using it.
Competitive Menu Pricing
Track how competing restaurants price the same dish category across cities and countries, instead of manually checking listings.
Franchise Performance Monitoring
Compare ratings and delivery-time performance across franchise locations in multiple countries to spot underperforming outlets early.
Cross-Country Market Research
Use multi-year rating and pricing history to understand how cuisine trends and delivery-fee behavior differ between markets.
Delivery-Fee Pattern Analysis
Analyze how demand-driven delivery fees fluctuate by city, time of day, and day of week to plan promotions around lower-fee windows.
AI and LLM Training Data
Use the clean, labeled Uber Eats Menu Dataset as training data for menu-item extraction and food-recommendation models.
Sentiment and Quality Research
Mine review-level data to understand what drives rating movement for specific restaurant types and cuisines across markets.
Who Actually Uses the Uber Eats Dataset
The Uber Eats Dataset provides food delivery and restaurant insights for market research, competitive analysis, pricing intelligence, and data-driven business decisions.
Restaurant Chains & Franchises
Benchmark menu pricing and ratings against competitors in the same cuisine and city, across multiple countries.
Cloud Kitchen Operators
Track delivery-only competitor pricing and rating performance to refine their own menu strategy.
AI & ML Companies
Source clean, structured global food-delivery data for recommendation engines and menu-understanding models.
Market Research Firms
Build multi-country category reports on the food delivery sector using multi-year historical depth.
Restaurant Consulting Firms
Validate menu-pricing and positioning recommendations with verified, current data.
Investment & Analyst Teams
Track delivery-fee dynamics and cuisine trends as alternative-data signals on the global food delivery sector.
What This Dataset Actually Saves You
Manually tracking menu prices, delivery-fee fluctuations, and ratings across hundreds of restaurants in multiple countries isn’t realistic without automation — and it definitely won’t catch a demand-driven fee spike or a sudden dip in ratings before it affects order volume.
Hours Back, Not Days
A structured feed replaces manual menu-checking entirely — what used to take a team days to compile is available on a schedule you set.
Forecasts Grounded in Real History
2+ years of price and rating data lets you separate genuine quality shifts from short-term promotional or demand-driven noise.
Catching Issues Before They Show Up in Orders
A dip in ratings often precedes a drop in order volume. A same-day feed means you catch the signal early enough to act, in any market.
Built Around Your Pipeline, Not Ours
Pick the countries, cuisines, and cadence that matter to you — delivered as CSV, JSON, API, or Parquet.
How the Uber Eats Data Collection Pipeline Actually Works
Here’s the straightforward version of how this gets built, not a marketing summary of it.
Collection Approach
Ethical, rate-limited collection from Uber Eats’ publicly visible pages across covered countries — no credential bypass, no restricted API access.
Validation Before Delivery
Every batch runs through automated schema checks, then a manual spot-check pass before it ships.
Deduplication
Repeat pulls of the same restaurant or menu item are collapsed into one clean historical record instead of duplicate rows.
Refresh Scheduling
Daily, weekly, or intraday cycles keep pricing, ratings, and delivery-time fields current — fast enough to capture demand-driven delivery-fee swings.
Stable Schema
Field names and structure stay consistent release over release, so your ingestion pipeline doesn’t break on the next refresh.
Compliance
Only publicly available platform data is collected, consistent with standard commercial data practice and platform terms.
This methodology runs on the same Food Delivery Data Scraping infrastructure behind our other restaurant-platform datasets — it’s a maintained pipeline, not a one-off project.
Delivered However Your Stack Expects It
WebDataInsights delivers the Uber Eats Dataset in flexible formats, enabling seamless integration with your existing systems, analytics tools, and data workflows.
CSV / Excel
Drop straight into Excel pricing models, Tableau dashboards, or Power BI reports.
JSON / API Feed
Documented schema, ready for direct ingestion into your application backend or data pipeline.
Cloud Storage / Database
Automated delivery to S3, SFTP, Snowflake, BigQuery, or Redshift on whatever cadence you set.
Pick the Plan That Matches Your Scope
Choose flexible Uber Eats Dataset pricing plans tailored to your data volume, update frequency, and business requirements.
One-Time Dataset Purchase
From $199
A one-time Uber Eats dataset snapshot, delivered right away — no subscription commitment.
What’s included:
- Full point-in-time dataset snapshot
- CSV, JSON, or Parquet delivery
- Filter by country and cuisine before delivery
Enterprise License
Contact Sales
Full-category, multi-country coverage with unlimited access and an SLA behind it.
What’s included:
- Unlimited API calls and delivery volume
- Dedicated account manager, 99.5% SLA
- Custom schema design and white-label delivery
A Team That’s Actually Built This Before
Reliable Uber Eats Delivery Dataset solutions backed by accurate food delivery data, scalable delivery, and actionable insights for market research and competitive intelligence.
Track Record at Scale
Millions of verified records collected and refreshed continuously across major global food delivery platforms — Uber Eats is one of several, not a one-off build.
Support That Doesn’t Disappear After Launch
Dedicated account management, SLA-backed delivery, and proactive alerts if a schema ever needs to change.
Compliance-First, By Default
Only publicly accessible platform data is collected — no credential bypass, no restricted-API workarounds.
Custom Builds When You Need Them
Need a field, cuisine, or country we haven’t listed here? We build custom Uber Eats dataset configurations on request.
Questions People Actually Ask Us About the Uber Eats Dataset
These come from real conversations with restaurant brands, analysts, and engineering teams evaluating this dataset.
It’s a structured export of Uber Eats’ global food delivery data — menu listings, pricing, ratings, delivery-time data, and customer reviews — cleaned, deduplicated, and delivered as CSV, JSON, or an API feed.
CSV, Excel (XLS/XLSX), JSON, and API feeds by default. Parquet is available on request for Spark, Databricks, Snowflake, or BigQuery pipelines.
Pricing and ratings can refresh intraday on enterprise plans, since delivery fees and menu prices change frequently. Menu listings update daily, and reviews update on a rolling basis.
Yes — tell us the countries, cuisines, fields, and refresh cadence you need, and we scope the extract accordingly rather than delivering a generic full export.
Yes, more than two years of historical menu pricing, delivery-fee, and rating data, useful for spotting genuine quality or pricing trends versus short-term fluctuations.
Yes. Because delivery fees on Uber Eats can shift with real-time demand, the Uber Eats Price Dataset tracks base delivery fee and demand-adjusted fee separately, with timestamps.
Yes. The Uber Eats Delivery Dataset tracks average and real-time delivery-time estimates by restaurant and area, along with historical trends by city and time of day.
Yes. Only publicly visible pages on Uber Eats’ platform are collected, using rate-limited, ethical methods. We don’t bypass logins or access restricted internal APIs.
A DIY scrape typically returns inconsistent, messy data that needs significant cleanup, especially when trying to normalize across multiple countries and currencies. This dataset is already normalized, deduplicated, and schema-stable, ready for analysis the same day it arrives.
Yes, delivery-only cloud-kitchen brands are flagged distinctly from dine-in restaurants, making it possible to benchmark them accurately against each other.
Uber Eats’ major markets, including the United States, United Kingdom, Canada, and Australia, with additional countries available on request.
Yes — automated delivery to S3, SFTP, Snowflake, BigQuery, or Redshift on whatever schedule you choose.
Get a Real Look at Uber Eats’ Global Food Delivery Data
3.4M+ verified records, daily pricing and ratings refreshes, and 2+ years of historical depth across Uber Eats’ global markets — starting at $199.