What is web scraping?

 

Web scraping, also known as web data extraction, is the process of retrieving or “scraping” data from a website. Data displayed by most websites can only be viewed using a web browser. Most websites do not provide the option to save the data which they display to your local storage, or to your own website. This is where a Web Scraping software like ScrapingAnt comes in handy.

Web scraping is the technique of automating this process so that instead of manually copying the data from websites, web scraping software performs action by a predefined algorithm. Unlike screen scraping, which only copies pixels displayed onscreen, web scraping extracts underlying HTML code and, with it, data stored in a database. In a non-automation world this kind of data retrieving can be performed as a common text copy-pasting action.
A web scraping software can automatically load, extract, and process any type of data from multiple pages of websites based on your needs. It is either custom-built for a specific website or is one that can be set up to work with any website.

Web Scraping Use Cases

  1. Retrieving of business contacts (email, name, website, address, phone, etc). The pretty common technique for creating lead generation database or marketing lists. Scraping targets for this case can be the following: Google Maps, Yandex Maps, Yellow Pages, ZoomInfo, Linkedin, etc.
  2. Retrieving of product details (price, images, reviews, etc). The product data allows companies to compare market competitors, create marketing strategies, make growth decisions, and many other eCommerce related cases.
    Common sites for scraping: Aliexpress, Amazon, Alibaba, eBay, a lot of Shopify stores, and the whole world of online stores.
  3. Collecting all types of data for Machine Learning. For the proper ML model training and validation data engineers need a lot of structured and quality input information. Pretty often the best way to collect the needed information is to employ web scraping specialists to get it.
  4. Odds scraping. Most betting companies can not rely just on their mathematical models to propagate different events market chances directly to users, so instead they also include in their models data from many different sources to spread understanding of probability.
  5. Search engines output scraping. Search engines operate with data that already retrieved by crawling a lot of sites, so when multi-site data harvesting is needed, sites like Google, Yandex, Bing, Baidu can be very handy to get exact links for scraping by interested keywords.

There are a lot of different niches and specific scraping usage scenarios, but we can track the global pattern:

  1. Find data source
  2. Get data from a source
  3. Analyze data

So web scraping is all about data.

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