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Data Parsing Explained 

by William Beel
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What happens behind the scenes of your web scraping projects might not intrigue you much; knowing about it can help you level up your scraping game, nonetheless. Data parsing is one of the most critical steps involved in web scraping. This is because the quality of your data heavily relies on the parser quality. Therefore, understanding data parsing can help you determine why some tools are better than others, allowing faster data navigation. 

That said, we’ll discuss an in-depth concept of data parsing below.  

What Is Data Parsing?

Data parsing is the process of transforming string data into a structured form. Simply put, it involves analyzing the data and structuring it per specific criteria, which is easy to understand and use. 

The entire process is divided into two separate components, lexical and syntactic analysis. 

The former takes a sequence of characters (strings) and converts it into tokens. Simply put, the parser turns meaningless unstructured data into a flat list of things like operator, string literal, or number literal using a lexer. 

Besides, it can discard whitespace and recognize reserved keywords. 

Syntactic analysis, on the other hand, takes the string tokens and sequences them into a parse tree, forming elements (nodes) and branches that depict their relation with each other.

In the web scraping process, parsing means:

  • To identify the information you need from an HTML document.
  • To structure the data that is easier to recognize, understand, and use. For instance, it might involve transforming the HTML into.csv or .json. 

Data Parsing and Web Scraping 

Consider you want to scrape a website. 

The first thing you’ll probably do is send a request to the server to access data on a website. Following this, you’ll save the HTML source to your PC. Sounds pretty simple, but is it really? 

The problem is that only a few HTML pages are neatly formatted when you download them. You’ll likely stumble across unsightly gibberish data that is almost unreadable. 

Fortunately, parsing a page makes it readable and comprehensible. Not only does it structure the data for you, but it also removes irrelevant useless data. 

The best part? The process does most of the heavy lifting for you. You can use parsing tools to explore further useful features like parsed document navigation using XPath or CSS selectors. 

Parsing a Web Page

When wanting to parse a webpage, you need to find features you want to scrape. Then, all you need to do is open the page in your browser and select “Inspect.”

As soon as you click, the website’s DOM tree will open. After identifying the presented information, you have to instruct the parser to navigate it. 

CSS and XPath selectors are two primary ways to do this. The next sections depend on the parsing tool you are using, nevertheless. 

Regardless of the case, you need to download the HTML source, extract the features you’ll choose with a parser, and save the output in your preferred format. 

Major Data Parsing Challenges

Data parsing is pretty straightforward and presents little to no challenges on a small scale. 

However, as with most web-scraping activities, it sometimes becomes tricky to manage. A few challenges are discussed below. 

  • JavaScript-generated pages. These pages often lack the typical features like class, making it challenging to extract and handle the needed data. 
  • Page structure changes. Giant websites, e-commerce websites, in particular, change their HTML frequently. In that case, you’d need to adjust the parser time and again because it breaks. 
  • Different formatting. If the data you’re willing to extract has distinct formatting across different pages, you might need to create custom parsing logic for recognizing and integrating it. 

Nonetheless, you can leverage the functionality of your scraping needs using headless browser automation. 

These browsers, unlike conventional browsers, work without a graphical user interface (GUI) and help you automate use cases using a command-line interface, resolving some of the major data parsing challenges.  

Several web scraping tools allow headless browsing. However, the most popular way to explore headless Chrome is via Puppeteer. 

Although a relatively newer platform, it allows a high-level API for controlling Chromium or Chrome. 

Using Puppeteer for Automated Web Scraping 

We recommend installing Puppeteer with npm because it’ll include the latest Chromium version that’ll work well with the library. 

Luckily, the Puppeteer tutorial for web scraping is pretty simple. All you need to do is create a project folder, import the puppeteer library in your script, and proceed. 

It will resolve a set of challenges that come with data parsing when scraping the web. 


Perhaps you cannot work effectively on raw data regardless of how skillful you are. For this reason alone, parsing is crucial to web scraping. 

Nonetheless, note that websites are built differently, making data parsing a bit tricky. However, you can use headless browsers to automate your scraping web activities and make them more efficient.

eb scraping software like Puppeteer allows seamless headless browsing. If you follow the right Puppeteer tutorial, you can resolve major data parsing problems. 

Web scraping software like Puppeteer allows seamless headless browsing. If you follow the right Puppeteer tutorial, you can resolve major data parsing problems. 

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