How Actually Does Web Scraping Work and What Is It Used For?
In 2025, 4.6 million gigabytes of data is generated every second, and by the time you’ve read this sentence, 15 million gigabytes of new data has already been generated.
Most of this data is scattered, unstructured, and impossible to use without the right tools.
That’s where web scraping steps in.
It’s the process of turning the internet’s chaos into meaningful, actionable insights. Whether you’re tracking market trends, training AI models, or fueling research, web scraping is the foundation of modern data-driven decisions.
In this article, we’ll unpack what web scraping is, how it works, and the role it plays in today’s fast-paced, digital world.
What Is Web Scraping?
Web scraping is the process of automatically extracting information from websites and organizing it into structured formats like tables or databases. Instead of manually gathering data page by page, web scraping tools handle the heavy lifting, making it faster and more efficient.
It’s how businesses monitor competitors, researchers gather insights, and developers power innovative tools; by turning scattered web data into something usable.
One strong point to highlight before moving forward, because legality of web scraping is often questioned:
Web scraping is not hacking.
It involves collecting publicly available information that’s already visible to anyone visiting a website.
Ethical scraping respects boundaries set by website owners, such as robots.txt
files, and avoids restricted or login-protected data. When done responsibly, web scraping becomes a powerful tool for accessing the vast potential of web data without crossing ethical or legal lines.
A Bit of History
The way data is collected has come a very long way.
Before the internet, businesses and researchers relied on time-consuming manual methods such as sorting through physical records, paper forms, and directories.
Even in the early days of the web, people would manually copy and paste information from websites.
Sounds like torture, I know.
As the web expanded, so did the need for faster, automated solutions, leading to the rise of web scraping as we know it today where you can extract data from thousands of pages in a manner of minutes.
Manual Data Collection vs. Web Scraping
Both manual data collection and web scraping aim to gather information, but their approaches and results are vastly different.
Manual collection might be sufficient for small tasks but falls apart when faced with larger datasets.
Imagine trying to record product prices from hundreds of e-commerce websites by hand.
It could take weeks, most of the data might be outdated by the time you’re done, and there’s a high chance of making irreversible mistakes along the way.
On the other hand, web scraping automates the entire process, gathering data accurately and at lightning speed.
For instance, a researcher analyzing customer reviews across dozens of platforms would be overwhelmed with a manual approach. With web scraping, they can extract the same data in a fraction of the time for faster and more reliable insights.
How Does Web Scraping Work?
Web scraping works by sending automated requests to a website, retrieving its content, and extracting the desired data (like text, images, or metadata) which is then organized into usable formats such as CSV or JSON. This process enables businesses, researchers, and developers to collect large amounts of information quickly and efficiently.
To truly understand web scraping, let’s break it down step by step—starting with how websites function and how scrapers automate these interactions.
Understanding HTTP Requests and Responses
The internet operates on a simple principle: when you visit a website, your browser acts as a client that sends a request to a server.
This request, made using the HTTP (or HTTPS) protocol, specifies the content the browser is asking for, whether it’s the HTML structure of a page, an image, or a JavaScript file. The server responds by sending back the requested data, which the browser processes and displays as the webpage you see.
Web scrapers replicate this process in an automated way.
Instead of a person manually visiting a site, a scraper sends a request to the website’s server just like a browser would. The server responds with the data, typically in the form of HTML.
The scraper then parses this HTML to extract specific pieces of information, such as product prices, article headlines, or customer reviews, and organizes it into structured formats for analysis.
For example, a scraper targeting a news website might extract headlines and article links from the page’s HTML. This data can then be saved in formats like CSV or JSON, enabling further analysis or integration into other systems.
A crucial part of this process involves tools and libraries that handle the various stages of scraping, from making HTTP requests to parsing and processing the data.
The Role of Tools and Libraries
Web scraping can be done in almost any programming language, but some are better suited for the task due to their performance or ecosystem of libraries and tools that have active communities.
Popular languages for web scraping include Python, JavaScript, Ruby, Java, and PHP.
Each of these languages offers specialized tools for handling different aspects of the scraping process, such as sending requests, parsing HTML, and managing dynamic content.
Request Libraries
Request libraries are the backbone of web scraping, responsible for sending HTTP requests to websites and retrieving their responses.
These libraries enable scrapers to simulate browser behavior and fetch raw HTML, JSON, or other data formats.
- Python:
requests
is the most widely used library for making HTTP requests, offering simplicity and reliability when scraping in Python. For asynchronous scraping,httpx
is a great alternative, allowing multiple requests to be handled concurrently. - JavaScript:
axios
andnode-fetch
are popular choices for web scraping in JavaScript, enabling scrapers to fetch content easily from web servers. - Ruby: Ruby’s
Net::HTTP
andRestClient
libraries are well-known for handling requests with minimal setup and code.
For example, in Python, using requests
to fetch a webpage looks like this:
# Import the requests library for handling HTTP requests
import requests
# Define the URL of the target website
url = "https://example.com"
# Send a GET request to the target URL and store the response
response = requests.get(url)
# Print the HTML content of the response received from the server
print(response.text) # Prints the HTML content of the page
Parsing Libraries
Parsing libraries process the raw HTML or other content retrieved by request libraries, allowing scrapers to extract specific elements or data points.
They help navigate the often complex structure of webpages to locate tags, classes, or attributes.
- Python:
BeautifulSoup
is the go-to library for parsing HTML, offering powerful tools for locating and extracting elements. For more performance-focused tasks,lxml
is a faster alternative andPandas
is much more useful for parsing tables. - JavaScript:
Cheerio
is widely used for parsing HTML in Node.js applications, mimicking the functionality of jQuery for easy element selection. - Ruby: Ruby developers commonly use
Nokogiri
, a versatile library for parsing HTML and XML content efficiently.
For instance, in Python, extracting an element from HTML with BeautifulSoup
looks like this:
# Import BeautifulSoup from the bs4 library
from bs4 import BeautifulSoup
# Define a simple HTML string
html = "<html><body><h1>Hello, World!</h1></body></html>"
# Parse the HTML string using BeautifulSoup and specify the HTML parser
soup = BeautifulSoup(html, "html.parser")
# Find the first <h1> tag in the HTML and extract its text content
print(soup.find("h1").text) # Output: Hello, World!
Headless Browsers
Headless browsers simulate a real browser environment without displaying a user interface, making them essential for scraping JavaScript-heavy websites or interacting with dynamic content. These tools can execute JavaScript, navigate pages, and interact with elements like buttons and dropdowns.
- Python:
Selenium
is the most popular headless browser in Python.Playwright
is a newer tool that supports multiple browser engines and is ideal for handling modern web technologies. - JavaScript:
Puppeteer
is the go-to library for JavaScript headless browser automation, designed specifically for controlling Chrome/Chromium. - Java: For Java developers,
HtmlUnit
is a lightweight and efficient headless browser.
For example, using Selenium
in Python to load a page and extract content looks like this:
from selenium import webdriver
driver = webdriver.Chrome()
driver.get("https://example.com")
print(driver.page_source) # Outputs the rendered HTML content
driver.quit()
Especially with the increasing complexity of websites and anti-bot measurements, combining different tools and libraries has become a core task in web scraping.
But why go to all this trouble?
Why are we scraping the web?
What Is Web Scraping Used For?
Web scraping is used to extract valuable data for insights, decision-making, and automation across industries. In 2025, it powers everything from training advanced AI models to monitoring market trends, transforming unstructured web content into actionable information.
Its versatility allows it to be applied to multiple use cases across a wide range of fields.
Let’s explore five general use cases where web scraping has become essential, starting with its role in fueling artificial intelligence:
1. LLM Training
Nearly half of all web scraping efforts in 2025 are focused on training large language models (LLMs).
Generative AI systems like ChatGPT or Google Bard or DeepSeek (the hot topic of these last days), require vast amounts of high-quality data, and almost all of this is collected through scraping public data on the web.
By extracting diverse datasets from sources such as news articles, research papers, and public forums, web scraping provides the foundational input needed to develop, fine-tune, and train these models.
With the rise of specialized and domain-specific LLMs, web scraping has become the driving force behind AI innovation of this decade.
2. Market Research
Web scraping is invaluable for market research, helping businesses gain a competitive edge by providing access to real-time insights.
Companies use it to monitor competitor pricing, track product launches, and analyze trends in consumer behavior.
For example, an e-commerce business can scrape product details and reviews from competitors’ websites to optimize its pricing strategy or identify gaps in the market.
3. SEO and Digital Marketing
Search engine optimization (SEO) and digital marketing heavily rely on web scraping to gather data for understanding their online visibility.
Scraping search engine results pages (SERPs) allows businesses to track their rankings, analyze keyword performance, and monitor competitors’ strategies.
Similarly, web scraping helps digital marketers track backlinks, identify content opportunities, and analyze trends in online advertising campaigns.
4. Data Aggregation
Web scraping simplifies the process of aggregating data from multiple sources into a unified format.
This is particularly useful for industries like e-commerce, where businesses need to consolidate product details, prices, and availability from various platforms.
Aggregated data is not only easier to analyze but also enables businesses to provide enhanced services, such as price comparison tools or product recommendation systems like PriceGrabber and Shopzilla, or even Google’s own platform Google Shopping.
5. Social Media Insights
Social media platforms are a goldmine of information, and web scraping is a popular method for gathering insights from these channels.
Businesses use it to monitor sentiment around their brand, track trending topics, and analyze audience engagement.
For instance, scraping X (formerly Twitter 🐦) posts and hashtags can help companies understand what the public thinks of their company and products and even track TTs to create campaigns that are relevant today.
To be accurate, use cases of web scraping are endless, and can be useful in any industry at any company size; or even on a personal level to handle daily tasks.
Web Scraping Landscape in 2025
Entering the second half of 2020s, web scraping is going to be harder to do at a scale and become an even more crucial part of every business.
As the demand for data has skyrocketed, so has the sophistication of both web scraping tools and the defenses against them.
If we look at the background of web scraping, we can understand how its come to be one of the most complex topics in web development.
The history of web scraping dates back to the early days of the internet when basic scripts were used to automate repetitive data collection tasks. With only the requests
library and a few lines of code, you would be able to scrape any website with ease.
Over the years, however, the web has evolved from static HTML pages to dynamic, JavaScript-driven experiences to enhance user experience of web visitors.
These quality of life improvements for web visitors meant new challenges to overcome for web scrapers, for example any dynamic webpage with JS rendering couldn’t be scraped using just HTTP requests.
And not all of these new challenges were done for the visitors, some were meant to outright block bots, including scrapers. Using Web Application Firewalls (WAFs) websites now could differentiate between a regular user and a scraping bot.
All of these pushed scrapers to evolve their workflows with tools like headless browsers and APIs.
Today, web scraping is no longer just about simple automation; it has become a critical infrastructure for industries ranging from AI to e-commerce.
While it was possible to run a web scraping operation with only a few hours of maintenance just a decade ago, today it’s a dedicated team operation if you’re doing it in-house.
There are many existing paid and free solutions to these challenges, so the more important question is:
How is web scraping going to change in the next decade?
AI and Large Language Models
One of the biggest shifts in 2025 is the role of web scraping in training AI systems and large language models (LLMs).
These generative models, such as ChatGPT, Google Bard, and Deepseek, depend on massive datasets sourced from the web.
Scrapers are now tasked with collecting a wide variety of data, from text on public forums to structured information from directories, aiding the development of domain-specific and generalized AI systems.
As AI continues to advance, web scraping will remain an irreplaceable part of data collection.
IPv6 and Proxy Management
The transition to IPv6 is reshaping the internet, with global adoption reaching 44.43% in 2025 and projected to surpass 50% by 2026.
This shift offers web scrapers access to a virtually limitless pool of IP addresses, enabling more effective proxy rotation and reducing detection risks. Countries like China are accelerating the move with initiatives such as the China Next Generation Internet (CNGI), aiming to phase out IPv4 entirely by 2030.
While IPv6 opens new opportunities for web scraping, it also introduces challenges.
Managing such a vast pool of addresses requires advanced proxy systems capable of handling both IPv4 and IPv6 environments seamlessly.
Additionally, not all websites fully support IPv6, and modern anti-bot defenses analyze behavior rather than relying solely on IP detection.
To stay ahead, scrapers need to adopt smarter proxy management solutions that dynamically allocate and rotate proxies, target specific geolocations, and maintain compatibility with IPv6 networks to avoid blocks.
AI-Powered Anti-Bot Systems
AI has unlocked a world of possibilities for scrapers, enabling advanced techniques like sophisticated parsing (using tools like ScrapeGraphAI) and dynamic adaptation to complex web structures.
But on the flip side**, it has become a powerful weapon in the hands of anti-bot systems**, making web scraping more challenging than ever.
Modern web application firewalls (WAFs) and anti-bot systems leverage AI to identify and block scraping activity with unprecedented precision.
These systems don’t just rely on simple IP bans or rate limiting; they analyze behavior patterns, traffic sources, and even SSL/TLS fingerprints to detect non-human interactions.
Cloudflare for example now allows website owners to block bots, scrapers, and crawlers with the single (virtual) push of a button.
Systems like these are constantly learning and adapting, making it harder for scrapers to stay under the radar.
The arms race between anti-bot systems and scraping technologies continues to evolve, pushing both sides to become increasingly sophisticated. For scrapers, staying ahead of these defenses requires not only technical expertise but also adopting tools like adaptive scraping APIs and intelligent proxy management systems to ensure uninterrupted access to data.
Conclusion
As industries become more data-driven, web scraping will continue to evolve, meeting the demand for faster, more reliable, and scalable solutions.
Being able to extract data from any website on the web is a huge plus on any web developer’s skillset, enabling them to create products and systems with extreme value.
The biggest ally of a developer in web scraping would be web scraping APIs that are designed to reduce months of deployment and regular maintenance efforts to just a few hours of setup.
Scrape.do is the best web scraping API in terms of cost, capacity, speed, and support.
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Frequently Asked Questions
What is an example of web scraping?
Imagine you’re a small business owner who wants to keep track of competitor pricing for a product you sell. Instead of manually visiting the competitor’s website daily to check prices, you can use web scraping to automate the process.
You just need to set up a small bot either through coding or no-code automation tools that:
- Visits the competitor’s website,
- Collects the product name, price, and availability information,
- And exports these information to a spreadsheet in your database.
Is web scraping still used?
Web scraping, especially with the rise of LLMs, is still used and now is in more demand than ever. It has significantly evolved in terms of practices and tools used, and the number of programmers that do web scraping has significantly increased in the last years.
Does web scraping need coding?
Contrary to popular belief and almost all resources online, you don’t need to know how to code to do web scraping thanks to no-code tools and APIs. However, without coding your capabilities are seriously limited when scraping the web, and with just a little bit of programming knowledge and the right tools you can scrape the web just like a pro scraper.