Uber Eats Sushi Restaurant Scraper, US Cities

Created by

DataCach

Actor

Ubereats Stores Search By Location And Keyword

Compare sushi delivery supply across four major US metros in one Uber Eats run. For strategy and site-selection teams making data-backed expansion decisions.

Try for free

Ubereats Stores Search By Location And Keyworddatacach/ubereats-stores-search-by-location-and-keyword

Store UUID

Store Name

Store URL

City

+8 fields

Input

Search locations(required):1600 Pennsylvania Ave NW, Washington, DC 20500, USA+3

Search keyword:Sushi

Max results per location:10

Output fields

Store UUID

Store Name

Store URL

City

Country Code

Estimated Time to Delivery

Rating

Rating Count

Store Images

Extraction Date

Extraction Datetime

Input

How it works

Sign up on Apify or log in

The Ubereats Stores Search By Location And Keyword Actor is ready to run with the inputs already set for "Uber Eats Sushi Restaurant Scraper, US Cities". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Uber Eats Sushi Restaurant Scraper, US Cities" task

The Ubereats Stores Search By Location And Keyword Actor will start running based on the input automatically. You can also change all the input fields, like search locations, search keyword, and more, to match your specific use case.

Receive the "Uber Eats Sushi Restaurant Scraper, US Cities" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as store uuid, store name, store url, and more.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Uber Eats Sushi Restaurant Scraper, US Cities" task via API.