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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
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                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
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                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                            [0] => en
                        )

                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
                )

            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
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                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
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                    [21] => userType
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.184
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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        )

    [raw:protected] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [postal] => Array
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            [registered_country] => Array
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                        (
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [ru] => США
                            [zh-CN] => 美国
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.184
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        )

)

 country : United States

 city : Columbus

US

Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Start Your Project

Uber Eats Food Delivery Menu Prices & Reviews - Uber Eats Web Scraping Datasets

Scrape valuable Uber Eats data insights using our curated Uber Eats datasets, encompassing restaurant details, menus, reviews, and ratings, empowering thorough analysis and strategic decision-making in the food service industry. Uncover

Reviews & Ratings
Cuisine Types
Location Data
Nutrition

Get Started

Uber Eats Food Delivery Dataset

The Uber Eats Food Delivery Menu Prices & Reviews Dataset allows for scraping restaurant data for prices, delivery options, and menus in multiple countries, including Japan, Italy, Germany, Canada, USA, Australia, UK, UAE, China, India, Switzerland, Qatar, Singapore, Ireland, Macao SAR, Luxembourg, Austria, Denmark, and Norway. This dataset provides valuable insights into consumer preferences and market trends, giving businesses a competitive edge in the food delivery market. It includes nutritional content, ingredients, allergens, and sourcing information to promote transparency and informed consumer choices. Additionally, this data can benefit health professionals, researchers, and policymakers when making nutrition, health, and sustainability decisions.

Uber Eats Datasets Explained

Use structured menu item extraction to gain insights, optimize product strategies, and understand customer preferences. Our collection of food datasets, Uber Eats food delivery data scraping, and food dataset extraction capabilities ensure accurate and up-to-date information on:

Restaurant Name
Restaurant Type
Menu Item
Cuisine Type
Price
Description
Ingredients
Allergens
Calories
User Ratings
Reviews
Delivery Time
Delivery Fee
Minimum Order Amount
Address
City
Country
Opening Hours
Closing Hours
Discounts/Offers
Contact Information
Payment Methods
Delivery Service Provider
Order Status
Packaging Details
Contact Details
Food Menu
Menu Image

Food-Datasets-Explained

Id	Restaurant_URL	Restaurant_Name	City	Address	Latitude	Longitude	Cuisines	Dining_Rating	Dining_Review	Cost_For_Two	Location	Opening_Hours	More_Info	Categorytree	Phone	Delivery_Rating	Delivery_Review	Fssai_No	Image	Menu_Type	Menu_Dish	Dish_Vote	Dish_Price	Dish_Description	Dish_Image	Tag
1	https://www.ubereats.com/r/sushi-zen	Sushi Zen	New York	123 5th Ave, NY	40.7142	-74.0059	Japanese, Sushi	4.7	250	40	New York - Midtown	11am–10pm	Sushi bar, omakase	Asian > Japanese	(212)555-0101	4.8	190	—	https://ubereats.com/sushizen.jpg	Dine-in	Sashimi Deluxe	150	$24.99	Fresh salmon, tuna sashimi	https://ubereats.com/sashimi.jpg	Sushi
2	https://www.ubereats.com/r/la-taqueria	La Taqueria	San Francisco	2889 Mission St, SF	37.7502	-122.418	Mexican	4.6	370	30	SF - Mission	10am–9pm	Famous tacos	Mexican > Tacos	(415)555-0202	4.7	300	—	https://ubereats.com/lataqueria.jpg	Dine-in/Takeout	Carne Asada Taco	500	$3.5	Grilled beef taco	https://ubereats.com/cartaco.jpg	Tacos

Flexible-Data-Delivery

Flexible Data Delivery

Tailored to your Uber Eats data intelligence needs, we provide flexibility in selecting output formats, storage options, and delivery schedules:

Access food datasets in JSON, CSV, and other preferred formats;
Retrieve data through SFTP or integrate directly with cloud storage solutions like Google Cloud Storage, AWS S3, and more;
Choose one-time, monthly, quarterly, or bi-annually extract food datasets frequencies to suit your requirements.

Get Started

Secure Premium Industry Data with Ease

Premium-Industry-Datasets-on-Demand

Premium Industry Datasets on Demand

Access Actowiz's curated enterprise datasets for precise data insights, sourced by top web data specialists.

Efficiency Meets Expertise

Efficiency Meets Expertise

Save time; our team expertly extracts data for analysis, aligning with strategic business goals cost-effectively.

Tailored Data Solutions for Your Enterprise

Tailored Data Solutions for Your Enterprise

Receive tailored industry datasets; our flexible methods align with unique business needs for optimal decisions.

Commitment-to-Ethical-Data-Practices

Commitment to Ethical Data Practices

Actowiz leads Ethical Web Data Collection, upholding GDPR and CCPA, setting standards for responsible data practices./p>

Pricing Overview

Standard Pricing

Standardized Data Offerings with readily accessible datasets.
Consistent Data Framework that ensures clarity and consistency.
Top-tier Data Integrity from the most intricate data channels.

Flexible Delivery Options:

Monthly Updates
Quarterly Deliveries
One-off Purchases

Get Started

Recommended

Custom Pricing

Tailored Data Retrieval precisely aligned with your business needs.
Adaptable Data Framework crafted to fit your unique needs.
Scalable & Dynamic Offerings to ensure flexibility and scalability.
Streamlined Communication ensures smooth and instant communication.

Varied Delivery Options:

Daily Updates
Weekly Snapshots
Monthly Insights
Quarterly Reports
Customized Frequencies to Suit You

Get Started

Inclusive Benefits Across All Plans:

Get Started

Expert Data Extraction
Regulatory Assurance
Personalized Support
Broad Data Coverage

Why Choose Actowiz Solutions’ Datasets?

Assessing Your Data Requirements:

Our first step is to delve into your company's specifics and business goals, ensuring we align perfectly with your data aspirations.

Crafting Tailored Data Solutions

Leveraging our robust in-house web scraping tools, we design a bespoke strategy tailored to your data extraction needs.

Sample Data Preview

Experience firsthand with a sample of our company data. Gauge its quality and familiarize yourself with our seamless data delivery mechanism.

Steadfast Data Streamlining

Once aligned, we initiate regular data dispatches based on the frequency and specifications we've mutually agreed upon.

Frequently Asked Questions

Uber Eats datasets refer to structured collections of data extracted from the Uber Eats platform. These datasets typically include information such as restaurant details (names, addresses), cuisine types, menus, ratings and reviews from users, operating hours, and geographical coordinates. They are used for various purposes including market analysis, consumer behavior insights, and strategic decision-making in the food service industry.

Choosing Uber Eats datasets over web scraping offers distinct advantages in efficiency, reliability, and compliance. These datasets provide structured and verified data, saving time and ensuring accuracy compared to the complexities of scraping and cleaning raw data. They include comprehensive information such as restaurant details, menus, reviews, and ratings, crucial for in-depth analysis and strategic decision-making in the food service industry. Using Uber Eats datasets also avoids legal concerns associated with scraping copyrighted or restricted content. Overall, Uber Eats datasets streamline data access, enhance reliability, and simplify integration into applications, making them an ideal choice for businesses and researchers seeking robust insights from the platform.

You can obtain Uber Eats data through our Uber Eats Datasets for purchase or subscription, providing comprehensive access to restaurant details, menus, reviews, ratings, and other valuable insights. These datasets are often used for market analysis, consumer behavior research, and strategic decision-making in the food service industry.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Trusted by Global Leaders – Secured by International Standards

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★

'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

2 min

★★★★★

“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”

Iulen Ibanez

CEO / Datacy.es

1 min

★★★★★

“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”

Febbin Chacko

-Fin, Small Business Owner

1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access icon

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

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Case Studies

Infographics

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Aug 15, 2025

Enterprise Price Intelligence: Building a Global Multi-Platform Scraping System

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Aug 14, 2025

Healthcare Review Analytics – Turning Patient Feedback into Insights

Actowiz Solutions turns patient reviews into actionable healthcare insights using AI-powered review scraping, sentiment analysis, and trend tracking.

Aug 13, 2025

How AI Job Market Data Scraping is Shaping U.S. Hiring Trends

Discover how AI-driven job market data scraping is transforming U.S. hiring trends with real-time insights, salary benchmarks, and demand forecasting from Actowiz Solutions.

Aug 12, 2025

E-commerce Price Wars: Amazon vs Walmart – Weekly Price Analytics

Discover how weekly price analytics reveal the Amazon vs Walmart e-commerce battle. Actowiz Solutions shares real-time price trends and competitive insights.

Read More

Tracking 5M+ SKUs Across 50 E-commerce Sites for Real-Time Price & Stock Monitoring

tracked 5M+ SKUs across 50 e-commerce sites for real-time price & stock intelligence to empower global retail strategy.

K-Beauty Market Intelligence: How Naver & Coupang Data Shaped a Turkish Importer’s Product Strategy

See how Actowiz Solutions used Naver & Coupang data to identify trending K-Beauty SKUs, prices, and stock insights for the Turkish cosmetics market.

How Compare Product Prices Weekly For Local Retail Stores Improved Sales and Customer Retention

Discover how our solution to Compare Product Prices Weekly For Local Retail Stores helped boost sales, enhance customer loyalty, and maintain competitive pricing effectively.

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India's Holiday Travel Surge Raksha Bandhan to Independence Day 2025

Explore India's holiday travel trends during the 2025 festive season—from Raksha Bandhan to Independence Day. Discover top destinations, travel tips, and booking insights.

2025 Buying Behavior Trends: Smarter Retail Decisions Backed by Data

Explore retail buying behavior trends for 2025 with data-driven insights on Gen Z, AI, omnichannel shopping, and smart decisions from Actowiz Solutions.

Real-Time Price Monitoring & Benchmarking on Amazon & Walmart for Smarter eCommerce

Use real-time price monitoring to benchmark Amazon & Walmart prices, avoid MAP violations, and power your eCommerce intelligence with Actowiz Solutions.

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weekly tracking of job role demand via Indeed & LinkedIn in Chicago, analyzing hiring trends, role popularity, and market demand shifts.

Monthly Tracking of Property Prices in NYC via Realtor.com

monthly tracking of property prices in NYC, using Realtor.com data to analyze market trends, price shifts, and neighborhood-level changes.

Weekly Uber Eats Data Tracking of Vendor Activity in New York

Analyze vendor trends with Weekly Uber Eats data in New York, tracking menus, pricing, and activity for strategic food delivery insights.

Read More