[Lulit Sharada]

Setting up scrapes for new listings by city or zip code helps me monitor real estate trends and see which areas are growing in popularity.

[Lulit Sharada]

• By parsing eBay’s JSON responses directly from network requests, I can extract product details like price, seller ratings, and product ID.

[Lulit Sharada]

• Setting up a schedule to scrape key financial metrics, such as PE ratio or dividend yield, helps monitor stock performance regularly.

[Lulit Sharada]

• Requests and BeautifulSoup can extract static HTML content, while Selenium is ideal for handling TUI’s dynamic pricing and availability displays.

[Lulit Sharada]

• Use Python’s Requests library to monitor network requests for JSON data, which often includes reviews and ratings, bypassing HTML parsing.

[Lulit Sharada]

• Since Mercari uses AJAX for some dynamic content, I combine Nokogiri with Watir to handle content that loads after page interactions.

[Lulit Sharada]

For dynamic content, like reviews and real-time stock information, Selenium with headless Chrome works well, especially when combined with randomized delays.

[Lulit Sharada]

Axios or Fetch can be used for JSON data retrieval, especially if network requests expose structured product data.

[Lulit Sharada]

Checking the site’s XML sitemap often reveals new product URLs, as they’re typically added to the sitemap immediately upon publication.

[Lulit Sharada]

• For dynamically loaded reviews, I use PHP with a headless browser integration, like Panther, to render full pages before scraping.

[Lulit Sharada]

• For Google Flights, I use automated notifications for specific routes and dates, reducing the need for constant scraping.