I need a robust scraping solution that continuously pulls price, full product descriptions, and customer reviews for electronics across roughly ten different e-commerce sites. Each time the script runs, the results should append to a master Excel workbook so that every entry is stored chronologically—allowing me to compare today’s prices with yesterday’s and build long-term trend charts without any manual work. Key expectations • The scraper must visit all target URLs, handle pagination or lazy-loaded content where it exists, and respect each site’s structure. • Collected fields: date/time stamp, site name, product name, current price, full description text, average rating, review count, and a link to the product page. • Excel output: one sheet that grows downward with every run; newest rows appear at the bottom. Each run should happen on a schedule I can adjust (cron, task scheduler, or similar). • Resilience: graceful handling of captchas or temporary blocks (rotating proxies or headless browsing accepted), clear logging of any skipped items, and an alert if a site layout changes. • Maintainable code: well-commented Python (BeautifulSoup, Scrapy, or Playwright are all fine) or an equivalent language you prefer, plus a short README explaining setup and how to add new sites later. Once delivered I will validate that: 1. Data from all ten sites lands in the workbook with a proper timestamp. 2. Prices from consecutive runs appear on separate rows, preserving history. 3. The script can be launched by a single command or scheduled task without manual tweaks. Let me know which libraries or frameworks you plan to use, any prior examples of similar large-site scraping work, and an estimate on turnaround time.