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For large-scale projects, I use multithreading to download multiple images simultaneously. This speeds up the process significantly compared to sequential downloads.

I use rotating proxies and headers to mimic human behavior. This helps avoid detection when scraping multiple movie details across a site.

For inconsistent metadata, I write conditional logic in my scraper to handle different cases. For example, I check for multiple possible class names or fallback values if the author name is missing.

I use WebSocket libraries like websocket-client in Python to directly connect to live data streams. This avoids the need for repeated page refreshes.

When dealing with large datasets, I use pagination to systematically scrape all pages. This ensures no listings are missed, even if they’re spread across multiple pages.

I manage pagination by detecting and following the “Next Page” button until no more pages are available. This ensures I capture all job listings without missing any.

Handling pagination in the Turo scraper is important for collecting data across all available car listings. Automating navigation through the “Next” button ensures a more comprehensive dataset, covering vehicles from different locations and owners. Random delays between page requests mimic real user behavior, reducing the chances of…

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For sites with dynamic content, I use Puppeteer to load all elements fully before scraping. This ensures I don’t miss products that are loaded asynchronously.