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A Simple Guide for Web Scraping

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The internet we love and cannot live without today has a marvelous history. From the fascinating days of ARPANET to meticulous development and constant updates of Internet Protocol, modern technologies allow everyone to enjoy the benefits and beauty of rapid online communication.

Today, the magic of wired and wireless transmission pushes the limits of efficiency and convenience. The information available at our fingertips can feel overwhelming and unmanageable. Thankfully, for modern problems, we have new solutions. Of course, it is hard to call easy access to data a problem. It is more of frustration caused by physical limitations to enjoy and most importantly – benefit from publicly available knowledge.

Of course, the abundance of data does not mean that every bit of it is useful. A large portion of the information on websites contains a comfortable presentation and design to make it attractive for the human viewer. Our goal is to find the solution that filters out the residue and helps to analyze data faster, concentrating massive amounts of it into accurate and applicable knowledge.

Fortunately, most things we think about on the web already exist. In this article, we will discuss web scraping and how automated tools for data extraction achieve our goals. Next, we will cover the steps of scraping, use cases for scraping bots, complementary tools, and types of usable software. For example, you can use popular coding languages to create and customize your tools or use a prebuilt no-code web scraper. Python is the most popular choice for scrapers and parsers, while bigger companies go for a no-code web scraper or outsource extraction tasks for professionals. For beginners, we recommend at least trying and creating your primitive software, and experimenting with its functionality and filtering capabilities to better understand the power of pre-compiled, no-code web scrapers. If everything sounds new to you, let’s start from the beginning.

How do bots scrape data from the web?

When we use digital devices to access a remote web server, everything happens so fast, but there is a ton of strict communication following internet protocol. A smooth and well-organized process of computer networking lets us enjoy the presented information in a rendered HTML format on a browser without the need to understand what is going on behind the scenes.

The goal of a web scraper is not to look for some backdoor for better acquisition of data – the complex communication is fast enough, and our manual navigation is what slows down the entire process. Scrapers eliminate the human factor and extract the renderable HTML file. When the process is automated, you ensure the fast connection of data from the most important pages or target valuable web servers repetitively to monitor changes in shown information.

Organizing data

If we would stop right here, the completed process would offer no value – so what if we have these HTML files downloaded, reading them only eliminates the need to get the packets on your device. After scraping, the information should travel to the pre-built parser. Data parsing is the thorn of the data extraction process that rarely submits to efficient automation. Creating software that filters and transforms knowledge into readable and understandable formats heavily depends on the structure of targeted pages. The parsing process needs human resources for adjustments and close observation if some sites become unparsable. However, with fast adjustments and parsers that work on desired websites most of the time, parses reorder data and make it ready for analysis. With big data sets, such as pricing tables from retailer websites, web scraping and parsing eliminate the time-consuming need to manually input information, accelerating the entire process and allowing us to derive valuable conclusions at a rapid pace.

Why do businesses need web scraping?

The benefits and examples discussed above encapsulate modern businesses’ obsession with web scraping. While recruiting technically proficient employees or at a larger scale – building data analytics departments are costly endeavors, modern businesses that understand their value and use cases cannot miss out on web scraping. Even with a no-code web scraper, an employee with enough technical skills can set up bots and reap the benefits.

Web scraping challenges

Although scraping and parsing are legitimate ways to collect and analyze information, the differences in manual user connections and the bombardment of data requests generated by bots are very visible. Their influx can slow down the server, forcing owners to use rate limiting and other protection measures to slow down or detect scrapers and ban their IP addresses. Even careful and rare extractions can trigger these defenses. To avoid problems and scrape without interruptions, use rotating residential proxies.

 This brief description of web scraping provides an abstract explanation of the process and its use cases, but it doesn’t do it justice. It is hard to describe the advantages of automated data extraction: the best way to understand it is to get your hands dirty. Try web scraping for your tasks, and you will soon appreciate its benefits.