I have a daily research routine for Daily Fantasy Sports that relies on current player-prop lines. To streamline the process, I need a production-ready pipeline that: • Scrapes the latest props each day from leading sportsbook sites as well as the major fantasy-sports platforms (these two categories cover every target URL I will supply). The data source is strictly web scraping, so please factor in rotating proxies, bot-avoidance tactics, and resilient CSS/XPath strategies. • Normalises the scraped fields into a clean schema—player, team, market type, line, over/under odds, timestamp—and writes them to a relational store. • Exposes that store through a lightweight REST-style JSON API so my internal models can hit a single endpoint and receive fresh records. • Surfaces key metrics in a browser dashboard: searchable tables, basic filters (sport, date, market), and a quick visual diff of line movement from the previous pull. Tech stack is flexible, though I am already running Python 3.11 on the rest of my tooling; libraries such as Playwright or Selenium for scraping and FastAPI or Flask for the endpoint will fit in smoothly, but I am open to your preferred equivalents. Front-end can be a simple React or plain-Vue single page app; again, choose what lets you deliver fast, readable code. Acceptance criteria 1. A single command or cron call fetches that day’s props and populates the database without manual intervention. 2. API returns the most recent record set in <500 ms for a typical request size. 3. Dashboard loads in any modern browser and updates after each daily scrape. 4. Clear setup guide and inline comments so I can deploy on a fresh Ubuntu server. If you have an existing scraper framework or dashboards you can demo, even better—just point me to it when you respond.