E-commerce Text Data Scraping

I need a robust web-scraping solution that can reliably pull 100,000 text-based records from a set of e-commerce sites. The data I’m after is purely textual—product titles, prices, descriptions, categories and any other publicly visible details that help build a clean catalogue. No images are required.

Here’s how I envision the job:

• You create an automated scraper (Python, Scrapy, BeautifulSoup, Selenium or a comparable stack) that navigates the target stores, handles pagination, variations and search filters, and respects reasonable crawl rates while bypassing blocks or CAPTCHAs when they appear.
• The script should be reusable: site list, category paths and output format must be easy for me to tweak later.
• Final deliverables: the fully commented source code, a requirements file or environment export, and a CSV or JSON file containing at least 100,000 unique product rows with the agreed-upon fields.
• Acceptance criteria: random spot-checks of 200 rows show