Learn How Web Scraping Works and Which Tools to Use

Confused and want to know what in the world web scraping is and how it works?

Well you’ve come to the right place because we’re about to lay down everything for you.

Before we dive in, I can already tell you the short version:

Web scraping is the process of extracting publicly available data from a website.

Join us to learn more about the specifics, how it works, and popular libraries that exist.

What is Web Scraping?

Basically web scraping is a procedure that allows you to extract a large volume of data from a website. For this it is necessary to make use of a “web scraper” like ParseHub or if you know how to code, use one of the many open source libraries out there.

After some time spent setting and tweaking it (stick to Python libraries or no-code tools if you’re new here), your new toy will start exploring the website to locate the desired data and extract it. It will then be converted to a specific format like CSV, so you can then access, inspect and manage everything.

And how does the web scraper get the specific data of a product or a contact?

You may be wondering at this point…

Well, this is possible with a bit of html or css knowledge. You just have to right click on the page you want to scrape, select “Inspect element” and identify the ID or Class being used.

Another way is using XPath or regular expressions.

Not a coder? No worries!

Many web scraping tools offer a user-friendly interface where you can select the elements you want to scrape and specify the data you want to extract. Some of them even have built-in features that automate the process of identifying everything for you.

Continue reading, in the next section we’ll talk about this in more detail.

How Does Web Scraping Work?

Suppose you have to gather data from a website, but typing it all in one by one will consume a lot of time. Well, that is where web scraping comes into the picture.

It is like having a little robot that can easily fetch the particular information you want from websites. Here’s a breakdown of how this process typically works:

  1. Sending an HTTP request to the target website: This is the ground on which everything develops from. An HTTP request enables the web scraper to send a request to the server where the website in question is hosted. This occurs when one is typing a URL or clicking a link. The request consists of the details of the device and browser you are using.
  2. Parsing the HTML source code: The server sends back the HTML code of the web page consisting of the structure of the page and the content of the page including text, images, links, etc. The web scraper processes this using libraries such as BeautifulSoup if using Python or DOMParser if using JavaScript. This helps identify the required elements that contain the values of interest.
  3. Data Extraction: After the identified elements, the web scraper captures the required data. This involves moving through the HTML structure, choosing certain tags or attributes, and then getting the text or other data from those tags/attributes.
  4. Data Transformation: The extracted data might be in some format that is not preferred. This web data is cleaned and normalized and is then converted to a format such as a CSV file, JSON object, or a record in a database. This might mean erasing some of the characters that are not needed, changing the data type, or putting it in a tabular form.
  5. Data Storage: The data is cleaned and structured for future analysis or use before being stored. This can be achieved in several ways, for example, saving it into a file, into a database, or sending it to an API.
  6. Repeat for Multiple Pages: If you ask the scraper to gather data from multiple pages, it will repeat steps 1-5 for each page, navigating through links or using pagination. Some of them (not all!) can even handle dynamic content or JavaScript-rendered pages.
  7. Post-Processing (optional): When it’s all done, you might need to do some filtering, cleaning or deduplication to be able to derive insights from the extracted information.

Applications of Web Scraping

Price monitoring and competitor analysis for e-commerce

If you have an ecommerce business, web scraping can be beneficial for you in this scenario.

That’s right.

With the help of this tool you can monitor prices on an ongoing basis, and keep track of product availability and promotions offered by competitors. You can also take advantage of the data extracted with web scraping to track trends, and discover new market opportunities.

Lead generation and sales intelligence

Are you looking to build a list of potential customers but sigh deeply at the thought of the time it will take you to do this task? You can let web scraping do this for you quickly.

You just have to program this tool to scan a lot of websites and extract all the data that is of interest to your customer list such as contact information and company details. So with web scraping you can get a large volume of data to analyze, define better your sales goals and get those leads that you want so much.

Real estate listings and market research

Real estate is another scenario where the virtues of web scraping are leveraged. With this tool it is possible to explore a vast amount of real estate related websites to generate a list of properties.

This data can then be used to track market trends (study buyer preferences) and recognize which properties are undervalued. Analysis of this data can also be decisive in investment and development decisions within the sector.

Social media sentiment analysis

If you are looking to understand the sentiment of consumers towards certain brands, products or simply see what are the trends in a specific sector within social networks, the best way to do all this is with web scraping.

To achieve this put your scraper into action to collect posts, comments and reviews. The data extracted from social networks can be used along with NLP or AI to prepare marketing strategies and check a brand’s reputation.

Academic and scientific research

Undoubtedly, economics, sociology and computer science are the sectors that benefit the most from web scraping.

As a researcher in any of these fields you can use the data obtained with this tool to study them or make bibliographical reviews. You can also generate large-scale datasets to create statistical models and projects focused on machine learning.

Top Web Scraping Tools and Libraries


If you decide to do web scraping projects, you can’t go wrong with Python!

  1. BeautifulSoup: this library is in charge of parsing HTML and XML documents, being also compatible with different parsers.
  2. Scrapy: a powerful and fast web scraping framework. For data extraction it has a high level API.
  3. Selenium: this tool is capable of handling websites that have a considerable JavaScript load in their source code. It can also be used for scraping dynamic content.
  4. Requests: through this library you can make HTTP requests in a simple and elegant interface.
  5. Urllib: Opens and reads URLs. Like Requests, it has an interface but with a lower level so you can only use it for basic web scraping tasks.


JavaScript is a very good second contender for web scraping, especially with Playwright.

  1. Puppeteer: thanks to this Node.js library equipped with a high-level API you can have the opportunity to manage a headless version of the Chrome or Chromium browser for web scraping.
  2. Cheerio: similar to jQuery, this library lets you parse and manipulate HTML. To do so, it has a syntax that is easy to get familiar with.
  3. Axios: this popular library gives you a simple API to perform HTTP requests. It can also be used as an alternative to the HTTP module built into Node.js.
  4. Playwright: Similar to Puppeteer, it’s a Node.js library but newer and better. It was developed by Microsoft, and unlike Windows 11 or the Edge Browser, it doesn’t suck! Offers features like cross-browser compatibility and auto-waiting.


I have never touched a single line of Ruby code in my life, but while researching for this post, I saw some users on Reddit swear it’s better than Python for scraping. Don’t ask me why.

  1. Mechanize: besides extracting data, this Ruby library can be programmed to fill out forms and click on links. It can also be used for JavaScript page management and authentication.
  2. Nokogiri: a library capable of processing HTML and XML source code. It supports XPath and CSS selectors.
  3. HTTParty: has an intuitive interface that will make it easier for you to make HTTP requests to the server, so it can be used as a base for web scraping projects.
  4. Kimurai: It builds on Mechanize and Nokogiri. It has a better structure and handles tasks such as crawling multiple pages, managing cookies, and handling JavaScript.
  5. Wombat: A Ruby gem specifically designed for web scraping. It provides a DSL (Domain Specific Language) that makes it easier to define scraping rules.


Just listing it for the sake of having a complete article, but don’t use PHP for scraping.

  1. Goutte: designed on Symfony’s BrowserKit and DomCrawler components. This library has an API that you can use to browse websites, click links and collect data.
  2. Simple HTML DOM Parser: parsing HTML and XML documents is possible with this library. Thanks to its jQuery-like syntax, it can be used to manipulate the DOM.
  3. Guzzle: its high-level API allows you to make HTTP requests and manage the different responses you can get back.


What are the libraries that Java makes available for web scraping? Let’s see:

  1. JSoup: analyzing and extracting elements from a web page will not be a problem with this library, which has a simple API to help you accomplish this mission.
  2. Selenium: allows you to manage websites with a high load of JavaScript in its source code, so you can extract all the data in this format that are of interest to you.
  3. Apache HttpClient: use the low-level API provided by this library to make HTTP requests.
  4. HtmlUnit: This library simulates a web browser without a graphical interface (aka it’s headless), and allows you to interact with websites programmatically. Specially useful for JavaScript-heavy sites and to mimic user actions like clicking buttons or filling forms.

Final Thoughts on This Whole Web Scraping Thing

I hope it’s clear now: web scraping is very powerful in the right hands!

Now that you know what it is, and the basics of how it works, it’s time to learn how to implement it in your workflow, there are multiple ways a business could benefit from it.

Programming languages like Python, JavaScript and Ruby are the undisputed kings of web scraping. You could use PHP for it… But why? Just why!?

Seriously, don’t use PHP for web-scraping, let it be on WordPress and Magento.

Alexander Schmidt

Alexander Schmidt is a software engineer who believes in working smarter, not harder. With 12 years of experience dealing with automation and web data extraction for analysis and research, he empowers businesses with practical tips and valuable insights delivered in a fun and easy-to-read manner to help others maximize the value and performance of their proxy solutions. When he's not tweaking his setup or consulting for SMBs, you can find Alexander geeking out on the latest tech news and AI advancements.

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