A Well-known bakery shop in Hawaii collects data about restaurants in the biggest metropolitan areas in the USA through site-specific data scraping.


A well-known bakery shop in Hawaii

Site-specific extraction and crawling

Challenge :

The client needed data about restaurants in the most significant metropolitan areas in the USA, providing a specific price range. The details to be incorporated into the structured menu included cuisine and demographic data.

They required easy access to complete restaurant location data from particular categories, food menus, and pricing.

It was challenging to constitute data while importing restaurant data for their business. They required precise data in the required format. As a result, they can easily upload it to the internal database to run a comparison engine and perform several monitoring activities.

The customer provided us with a list of resources to be scraped, required data points, and data extraction frequency for everyday jobs.

The team X-Byte has set restaurant location scraping APIs for fetching the necessary location data from a particular source website.

The client wanted scraped data in the CSV format and uploaded it to the S3 servers. The early setup was complete within a few days, and the crawlers began delivering the necessary data instantly.



  • Set up the Crawler: Initially, the crawler was set to scrape restaurant location data and other necessary data fields for predefined categories within an automated style every day.
  • Data Template: : Depending on the schema given by the customer, a template was made using data structuring that would happen.
  • Delivery of Data: The concluding data was delivered within an XML format through Restaurant Location Data Scraping API-based daily without manual involvement from either side.

Every record inside the dataset had all the information, i.e., Restaurant’s Name, Address, City, State, Zip Code, Fax, Latitude & Longitude, Phone, Opening Hours, Ingredients Picture, Food Name, Price, Type, Description, Promotion Details, Delivery Price, Category, Website, Working Hours, Star Ratings, and Number Of Reviews.


Setting up the Crawler The crawler was initially configured such that it could automatically scrape product price and essential data fields for present categories on a daily basis.

Data Template : A template was created utilizing data structuring based on the schema provided by the customer.

Delivery of Data : Without any manual input from either side, the closing data was supplied in an XML format through Data API regularly.

The dataset had all the information including comments, news timelines, most viewed articles, customer behaviour, etc. All of the scraped data was indexed using hosted indexing components, and search APIs were made available so that a client could get the results every few minutes.


  • End-to-end solutions for this non-tech client
  • All the complexities within source websites are taken care of
  • Data imported in a day while placing an order
  • No task force associated with the client’s end
  • Any alterations within the resource websites were managed, and clients were distracted from such problems.
  • Lower data turnaround time has improved the capability of market client’s capabilities and services
  • Productivity improved as the data team might work on some other projects. The client extended into other business verticals.