Case Study – Scrape Restaurants and Menu Data from DoorDash

Our client has a restaurant business with a significant online presence. They wanted to increase online visibility with enhanced menus to gain high customers by leveraging engaging visuals, detailed descriptions, and interactive features. They wanted complete location data from particular categories, food menus, and pricing.

The main challenge in DoorDash restaurant data scraping was that the client required information about restaurants catering to a particular price range. Along with the demographic and cuisine details, the data must include a complete menu in a structured format.

The dynamic website structure of DoorDash made it challenging to extract data accurately, as the structure and data keep changing dynamically.

The menu items across restaurants vary in formats and categories. Hence, it was tricky for us to maintain the consistency of the data while performing online food delivery app data scraping.

• Our clients provided us with a list of resources to scrape from DoorDash.
• Our team then set the restaurant menu scraping APIs for fetching the critical data from the website.
• Initially, to scrape restaurant and menu data from DoorDash, set the crawler to scrape restaurant and other necessary data fields for defined categories in an automated style every day.
• Based on the schema provided by the customer, we first designed a template using data structuring.
• We delivered in CSV format and uploaded to S3 servers without manual involvement. The dataset's record includes the restaurant's name, address, contact details, opening hours, food name, price, type, description, delivery price, website, working hours, ratings, and reviews.

• Before starting our scraping procedure, we thoroughly studied DoorDash's terms of service and adhered to the legal considerations.
• Next, we discussed with our client to finalize the specific data requirements.
• We then analyzed the DoorDash website's structure and found the HTML elements containing desired data.
• Next, we created a Python project by installing the necessary libraries.
• Then, we build a scraping script to send HTTP requests to the DoorDash website for HTML parsing.
• We decided to store the data in CSV format, hence, implemented the necessary logic to save the scraped data.
• Finally, we executed our scraping script and monitored its progress continuously.

Customized Scraping Solutions: Food Data Scrape provides an end-to-end solution for clients to meet specific business requirements using Food delivery scraping API. Understanding each client's unique needs, we provide customized scraping strategies and methodologies accordingly.

Scalability: We can easily handle large volumes of data efficiently and scale up our operations to promptly scrape and process extensive data.

Advanced Scraping Tools & Technologies: We employ advanced scraping tools and technologies to extract data accurately and efficiently.

Compliance and Legal Considerations: We respect the website's copyright and privacy. Hence, our scraping process is completely able with ethical and legal considerations avoiding any risks of legal complications.

Data Delivery and Formats: Delivery of our scraped data is in multiple formats, including CSV, Excel, JSON, and XML depending upon the client's need

Final Outcomes: We successfully scraped the restaurant and menu data from DoorDash. By leveraging the scraped data from DoorDash, our client enhanced the accuracy and timeliness of its restaurant information. Customers could view updated menus, prices, and availability, allowing them to make informed choices and place orders confidently. Overall, it enhanced user experience and increased customer satisfaction with their platform.