Data scraping or web scraping is the process of extracting information from a website into a spreadsheet or a local file on your computer or database.
What Is Data Scraping?
In the world of computer science, data scraping, often known as web (online) scraping, is a way of extracting data from websites to save it on local databases or on other applications using computer software.
A typical application of data scraping is to collect content, pricing, or contact information from internet sources.
Two major components make up data scraping – the crawler and the scraper.
A web crawler, often known as a “spider,” is an artificial intelligence (AI) mechanism that uses hyperlinks and search engines to scan and search for data on the internet, much like a human will do in their free time. When relevant data is found, it is sent to the web scraper.
A web scraper is a specialized tool that extracts data from an online web page. The data beacons in the web scraper are used to identify the data that you wish to extract from the HTML file – generally, XPath, CSS selectors, regex, or a combination of these protocols are used in the process.
Scraping data is a useful technique for staying ahead in the business world. Consider a business that spends money on product marketing to increase sales, but is unaware that their competitors are many steps ahead of them by employing business automation technologies and a web scraper. The web scraper can quickly identify a competitor’s new pricing as soon as it appears online, allowing them to respond quickly and maintain their dominance on the market intact.
Although online scraping can be done manually, automated methods are usually preferable for scraping web data since they are less expensive and work faster.
Web scraping, on the other hand, is not always an easy process. Since websites come in a variety of shapes and sizes, it is mandatory to check if your web scrapers’ functionality and capabilities align with the requirements of the sites.
Web scraping is mostly used in e-commerce and sales to track prices and generate leads. However, many investors are beginning to use this technology in online financial transactions these days. It automates the extraction of data from a variety of sources and saves the information in a structured manner for systematic review.
While data scraping technologies have legitimate legal uses, they can also be used to collect and reinterpret data for unlawful purposes, including identifying pseudo-anonymous web service users or plagiarizing branded material. Spammers and fraudsters frequently utilize data scraping techniques to collect email addresses to send spam emails. It is also used to get into websites or corporate intranets and acquire information to conduct additional crimes, such as blackmail or fraud.