Ecommerce web scraping involves extracting data from online retail platforms such as Amazon, Walmart, and eBay, primarily using automated tools or scripts. This data, which includes product details, pricing, customer reviews, and seller information, aids businesses and researchers in analyzing market trends, tracking price fluctuations, and studying competition for strategic planning. There are different types of ecommerce scraper tools, including custom scripts, no-code scrapers, web scraping APIs, and browser extensions. The tutorial provides guidance on building a custom Python scraper using libraries like Requests, Beautiful Soup, and Selenium. Challenges faced in ecommerce web scraping include dynamic page structures, diverse product page layouts, and anti-scraping measures like CAPTCHAs. To address these challenges, advanced techniques and tools like Playwright Stealth or dedicated ecommerce scraper APIs are recommended, which help in bypassing blocks and retrieving structured data without managing servers or proxies. The article also highlights various scraper APIs tailored for platforms like Amazon, eBay, Walmart, and others, offering comprehensive data collection capabilities.