[Soma Danilo]

Storing scraped product data in a database like MongoDB or SQLite allows for efficient querying and analysis. This approach makes it easier to track price trends, compare products, and analyze customer ratings over time. It’s also scalable for handling large datasets.

[Soma Danilo]

Handling missing data or unexpected changes in the site’s structure is crucial for building a robust scraper. Websites like RottenTomatoes frequently update their layouts, which can break hardcoded scripts. Using flexible selectors that rely on attributes or patterns rather than static class names can mitigate this issue. Regularly testing and logging the scraper’s performance ensures it remains functional over time.