Data Scraping from Atlantic Retail Tenant Listings

Project Description:

The goal of this project is to automate the extraction of tenant information from the Atlantic Retail website's tenant directory (https://atlanticretail.com/tenant/), and store this data in a structured MySQL database. This project involves web scraping to retrieve key details such as tenant name, business type, contact information, website, and other relevant data listed on the website.

The scraping process will be implemented using Python with libraries such as BeautifulSoup and Requests for HTML parsing and data extraction. The scraper will crawl through the directory pages, identify tenant listings, and extract required information into a structured format. The extracted data will be cleaned, validated, and formatted to ensure consistency.

Once the data is gathered, it will be inserted into a MySQL database. The MySQL schema will include relevant tables and fields to hold the tenant information efficiently, ensuring scalability and easy access for future data queries.

To ensure data integrity and accuracy, periodic scraping and database updates can be implemented. Additionally, error handling, logging, and notifications will be incorporated into the system to monitor the scraper’s performance and manage potential issues like changes in website structure.