Scraping data from Target's eCommerce site poses significant challenges due to its dynamic content, lazy loading, and robust blocking system. To address these issues, the guide explores multiple methods of extracting product listings. It begins with Python techniques using Requests and BeautifulSoup, which provide limited success due to the inability to fully render pages without a browser. The guide then introduces Selenium to enhance scraping efficiency by rendering pages like a browser, resulting in more comprehensive data extraction. Additionally, the guide explains how to leverage Claude with Bright Data’s MCP Server for an even more efficient and detailed scraping process. This approach involves configuring the MCP connection and utilizing AI tools to automate the task effectively, allowing for better extraction results with less manual intervention and code. The comparison of methods highlights the importance of using advanced tools and techniques for overcoming the complexities of scraping modern web pages like Target's.