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Web scraping has become a popular technique for extracting data from websites and other online sources. Tools like UiPath and Automation Anywhere offer powerful features for automating web scraping tasks, including the ability to extract data from dynamic websites and handle complex authentication and pagination.

While these platforms offer many advantages, some developers prefer to run their own custom web scrapers in the cloud using programming languages like Python. One of the main advantages of running custom web scrapers is the flexibility and customizability it offers. By building a scraper from scratch, developers can tailor the tool to their specific needs and extract data in a way that is optimized for their particular use case.

Another advantage of running custom web scrapers is the ability to run them in the cloud, which offers benefits such as greater scalability, reliability, and accessibility. By deploying web scrapers on cloud platforms like AWS or Google Cloud, developers can take advantage of powerful computing resources and easily scale their scrapers up or down as needed.

Although webscraping sounds very attractive, there are some challenges to consider. For example, building and maintaining a custom scraper can be time-consuming and requires a strong understanding of web technologies and programming languages like Python. In addition, web scraping can be legally and ethically complex, and it is important to carefully consider the legality and ethics of scraping a particular website or source of data.