What Is Web Scraping and How Does It work?

The importance of web scraping increases as the size of the digital economy increases. Learn more about online scraping, how it works, and why it's so important for data analytics by reading on.

Everyday life is getting more and more replete with data. Data analytics are becoming a fundamental part of how firms are run as a result of this growth. The internet is the largest reservoir of data, despite the fact that it comes from many different places. Companies require data analysts who can scrape the web in increasingly complex ways as the areas of artificial intelligence, big data analytics, and machine learning advance.

What is web scraping?

Web scraping is a technique for obtaining information and data from the internet (also known as data scraping). This information is often preserved in a local file so that it can be altered and analysed in the future. Web scraping is a much smaller-scale version of copying and pasting content from a website into an Excel spreadsheet.

However, when individuals use the term "web scrapers," they typically refer to computer applications. Web scraping software (sometimes known as "bots") is designed to browse websites, scrape the pertinent pages, and extract meaningful data. These bots can quickly extract enormous amounts of data by automating this procedure.

What types of data are available for web scraping?

If there is data on a webpage, it might theoretically be scraped. A few examples of the typical data types that businesses collect include pictures, videos, text, product specifications, user comments and reviews (available on websites such as Yelp, or TripAdvisor), and prices from websites that compare prices. The types of data you can scrape are subject to some legal limitations, but we'll cover those in more detail later.

What is web scraping used for?

There are many applications for web scraping, notably in the field of data analytics. Companies that conduct market research use scrapers to collect information from online forums or social media for purposes like customer sentiment analysis. To support competitor analysis, some people scrape data from product websites like Amazon or eBay.

Web scraping is a common practice used by Google to evaluate, rank, and index their content. Using web scraping, they can also take information from other websites and transfer it to their own.

Numerous businesses also engage in contact scraping, which is the process of searching the internet for contact information to be utilised for marketing. If you've ever agreed to use a company's services in exchange for giving them access to your contacts, you've already given them consent to do this.

There aren't many limitations on what can be done with web scraping. It depends on your creativity level and what you want to accomplish. The list is pretty much unlimited and includes everything from weather data to real estate listings to performing SEO audits!

It should be mentioned, nevertheless, that web scraping also has a murky side. In order to commit fraud, frauds, intellectual property theft, and extortion, bad players frequently scrape data such as bank details or other private details. Before beginning your own web scraping journey, it's wise to be aware of these risks. Make sure you stay up to date on the laws governing online scraping.

How do web scrapers work?

Web scraping is the process of obtaining data from predetermined websites. Therefore, the URLs of the websites that are intended for scraping are sent to the scraper. The necessary data, like prices or customer reviews, is extracted from the HTML code of these web pages.

The data is subsequently output by the scraper in a format that can be read. It saves it in a spreadsheet or database for later analysis.

The majority of websites ban scrapers to avoid them from slowing the site, hence web scrapers are powered by proxies to get around restrictions. The two types of proxies that are most frequently used are residential and data centers. By serving as a middleman, the proxy prevents communication between your computer and the web server directly.

With the proxy server, you can scrape information from geo-blocked websites because it has an IP address associated with a different region.

Tools for web scraping

We've discussed the fundamentals of web data scraping, but how does this process function technically? Web scraping frequently calls for some familiarity with programming languages, with Python being the most widely used. Fortunately, Python comes with a tonne of open-source packages that greatly simplify web scraping. These consist of:

BeautifulSoup

A Python package frequently used to extract data from XML and HTML texts is called BeautifulSoup. Large swaths of data are considerably easier to navigate and search through because of BeautifulSoup's organisation of this processed stuff into more user-friendly trees. It is frequently the data analyst's preferred tool.

Scrapy

A Python-based application framework called Scrapy scans the web and pulls structured data from it. It is frequently employed for information processing, data mining, and the preservation of historical content. It can be employed as a general-purpose web crawler or to extract data through APIs in addition to web scraping, for which it was specially created.

Pandas

Another versatile Python toolkit for data processing and indexing is called Pandas. It can be used in combination with BeautifulSoup to scrape the web. One language can be used by analysts to complete the whole data analytics process when utilising pandas.

Parsehub

If you're not a seasoned programmer, here's a supplementary tool! It is simple to scrape online data using Parsehub, a free online application that isn't a Python package. The only drawback is that you must pay to use everything to its full potential. But the free tool is fun to experiment with, and the business has top-notch customer service.

Conclusion

Other tools are readily available, ranging from those used for general-purpose scraping to those created for more intricate, specialised jobs. The best course of action is to investigate which technologies best fit your interests and skill level before incorporating them into your data analytics toolbox!