4 Web Scraping interview questions and answers

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Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.
There are many specific reasons why businesses may want to scrape a website; one of the vital reason being the unavailability of APIs. Some of the other major reasons which may lead a company into scraping website are: Expand Market Share Due to the lack of availability of APIs the possibility of collaborating with business partners is limited. By exposing the data available in their website as APIs enterprises can open up new channels, possibilities to expand the market share and increase sales. Enter New Markets with Early go-to Market Strategy API being the long time strategy, Web Scraping solution can potentially enable organizations to build an early go-to market strategy. Access to Renewed and Structured Data Scraping the website of the organization through a Web Scraping solution gives an organization the chance to access renewed, structured and up to date data through the scraped APIs.
Web scraping is used for contact scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.
Using data scraping you can build sitemaps that will navigate the site and extract the data. Using different type selectors you will navigate the site and extract multiple types of data - text, tables, images, links and more.
When you are going to crawl large-scale websites, then efficiency, scalability and maintainability are the factors that you must consider. Crawling large-scale websites involve many problems: multi-threading, I/O mechanism, distributed crawling, communication, duplication checking, task schedule, etc. And then the language used and the frame selected play a significant role at this moment. PHP: The support for multithreading and async is quite weak and therefore is not recommended. Node.js: It can crawl some vertical websites. But due to the support for distributed crawling and communications, it is relatively weaker than the other two. So you need to make a judgment. Python: It’s Strongly recommended and has better support for the requirements mentioned above, especially the scrapy framework. Scrapy framework has many advantages: Support for XPath, Good performance, Has debugging tools

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