Web scraping involves automatically collecting data from websites for various purposes, such as data analysis or enhancing AI models, and Python is commonly used for this task due to its robust scraping libraries like lxml. The lxml library extends Python's capabilities by providing efficient parsing of XML and HTML documents, making use of fast C libraries and integrating with Python’s ElementTree for hierarchical data processing. However, manual web scraping using lxml can be time-consuming and costly, especially for complex websites or large data volumes. As an alternative, Bright Data offers pre-collected datasets and Web Scraper APIs that simplify the process by reducing time and costs involved in data collection, providing tools that handle proxy management and CAPTCHA solving. The article explains the process of using lxml for both static and dynamic web content, demonstrating techniques for extracting data from specific web elements and saving the results in JSON format. It also highlights the use of Selenium for scraping dynamic content and Bright Data's proxy services to overcome scraping challenges such as rate limiting and geoblocking.