Web Text Scraping into Database

I need all relevant textual content lifted directly from a defined set of web pages and delivered in a clean, structured format that can be dropped straight into a database. The sites are publicly accessible, but the information is spread across multiple pages, so the scraper will have to crawl links, respect [login to view URL], and handle pagination. Because my ultimate goal is to fill a database, please organise the scraped data into a tidy CSV or JSON with consistent field names. If you have a preferred schema or can recommend the best relational or NoSQL store for this volume, I’m open to suggestions—flexibility here is an advantage. Key requirements • Source: web pages only (no APIs or PDFs involved) • Content: text data exclusively; no images are needed • Approach: any reliable stack you’re comfortable with—Python + BeautifulSoup/Scrapy, Node + Cheerio/Puppeteer, or similar—as long as the code is well-commented and repeatable • Respect polite scraping practices (rate limiting, user-agent, retries) Deliverables 1. Scraping script(s) with clear setup instructions 2. The fully extracted dataset in CSV or JSON 3. Brief read-me outlining how to rerun the scraper and import the results into a database I will validate the output by spot-checking several pages for accuracy and by running the script myself to confirm it reproduces the same dataset.

Project ID: 40307140