Data scraping: What it is and how it works

These days, so much of the information we see online is collected quietly in the background. It powers everything from business decisions to research projects and even the personalized recommendations you get on your favorite sites.

But all this data collection naturally leads to questions: Who’s gathering it? How are they doing it? And how can you keep your own information safe?

This guide takes you through data scraping in clear, simple terms, from the tools people use to how it differs from things like web crawling or hacking. You’ll also see common real-world uses, learn about the legal and ethical side of things, and get tips on protecting your data from unwanted scraping.

What is data scraping

Data scraping is all about gathering information from different sources, like websites, databases, or documents, so you can use it for things like market analysis, research, or business planning. It covers any approach you might take to collect data, whether that’s doing it by hand or using automated tools.

It’s a pretty broad term. At its core, it just means finding the data you need and getting it into a usable form. One of the most common types is web scraping, which usually involves automated techniques to pull specific details from web pages quickly and efficiently.

How does data scraping work?

Think of data scraping like sending out a request for a webpage, just as your browser does when you visit a site. But instead of showing you the page, a scraping tool grabs the underlying content (usually in HTML) and digs through it to find what you want.

This process is called parsing. It’s like giving the program a set of instructions that tell it what to look for and pull out. For example, you might have it extract product names and prices from an online store.

Manual vs. automated scraping

There are two main ways to scrape data, including manual and automated:

• Manual scraping is just what it sounds like: someone browsing sites and copying the information they need by hand. It’s fine for very small projects, but it’s slow, tedious, and not practical for collecting large amounts of data.
• Automated scraping uses specialized software, bots, or scripts to do the work for you. These tools can process web pages much faster and more reliably than a human ever could.

Since most real-world data scraping relies on automation, this guide focuses on automated scraping: how it works, the tools involved, and how to do it responsibly.

Common data sources and formats

When it comes to data scraping, there are two big questions: where does the data come from, and how is it stored once you have it?

Common data sources

Scraped data can come from all kinds of places, including:

• Public websites and online directories.
• APIs that provide structured data.
• Documents or files (like PDFs or spreadsheets).
• Databases, when access is available.

Common output formats

Once collected, the data is usually organized into structured formats that make it easy to store, analyze, or share. Some of the most popular formats include:

• CSV (Comma Separated Values): A simple, tabular text format often used for spreadsheets.
• JSON (JavaScript Object Notation): A lightweight, human-readable data interchange format, common for APIs and web applications.
• XML (Extensible Markup Language): A widely used markup language for encoding documents in a format that is both human-readable and machine-readable.
• SQL (Structured Query Language) tables: Generally used to manage and query relational databases.

How is data scraping done?

Data scraping can be done in a variety of ways, depending on the kind of website you’re working with and the type of data you want. Some methods are pretty straightforward, while others are designed to handle complex sites that load content dynamically or try to block scrapers. Let’s look at some of the most common techniques.

HTML and DOM parsing

One classic approach involves working with the raw HTML code of a webpage. When a scraper requests a page, it gets back the underlying HTML, which describes all the elements you see on the site.

Scraping tools use libraries to turn that HTML into a Document Object Model (DOM), which is a tree-like structure that lays out all the page’s elements in a way that’s easy to navigate. Once you have the DOM, you can move through it systematically to extract just the pieces you need, like article headlines.

This approach works especially well for static web pages, where all the content is present in the HTML that loads right away.

XPath and regex

Scrapers often need to pinpoint very specific bits of information on a page. That’s where XPath and regex come in handy:

• XPath (XML Path Language) is like a query language for HTML. You can use it to navigate the DOM and select exactly the elements you want, even if they’re buried deep in the page’s structure.
• Regex (regular expressions) is all about pattern matching in text. It’s great for pulling out things like phone numbers, emails, or product codes from a block of text by looking for predictable patterns.

API-based scraping

Data scraping doesn't always mean extracting raw HTML. Many websites offer APIs (application programming interfaces) designed to share data in a clean, structured way.

If a site provides an API, it usually means they’re making certain data available on purpose, often in formats like JSON or XML. You can send a request to the API and get back neatly organized data that’s easy to use and doesn’t require parsing messy web pages.

Using APIs is generally more reliable and less likely to break if the site layout changes. Plus, it’s often within the site's rules and terms of service.

Browser automation tools

Modern websites heavily rely on JavaScript to dynamically load content. A lot of content doesn’t show up in the initial HTML at all but loads later as you interact with the page. That can make simple parsing useless, as there’s nothing there to grab.

However, automation tools like Selenium and Playwright can assist with this. They control a real web browser and simulate human actions like clicking buttons and scrolling down pages to open dynamic elements. Once everything is fully loaded, the scraper can grab all the data it needs.

Use of VPNs and proxies in scraping workflows

Many websites use anti-scraping measures like IP blocking, CAPTCHAs, or rate limiting to slow down or stop automated data collection. To get around these defenses, scrapers often rely on proxies and VPNs to disguise their traffic and avoid detection.

• Proxies work as intermediaries between your scraper and the target website. When you use a proxy, your requests get routed through a separate server, hiding your real IP address. By rotating through many proxy servers, scrapers can make their traffic look like it’s coming from different locations, spreading out requests and reducing the chances of getting blocked for too many hits from a single IP.
• VPNs (virtual private networks) take this idea a step further. A VPN encrypts all your internet traffic and routes it through a secure server in a location of your choice. This not only changes your apparent IP address but also encrypts the connection itself, protecting it from snooping and interception. That encryption and security make top-notch VPNs like ExpressVPN a stronger choice in many cases, especially when privacy is a concern.

Learn more: For many scraping workflows, VPNs are the more reliable option. Read more about proxies vs. VPNs to help you make an informed choice.

Disclaimer: We do not encourage or endorse any scraping practices that violate website terms of service or applicable laws.

Data scraping vs. web crawling vs. hacking

Even though they all involve interacting with online data, scraping, web crawling, and hacking each have very different goals, methods, and legal or ethical implications.

Web crawling

Web crawling is what search engines like Google do to help you find information online. A crawler (also called a spider or bot) systematically browses the internet, following links to discover new and updated pages.

The main goal is to build a comprehensive index of the web so users can easily search for what they need. This process is generally seen as helpful and cooperative because it connects people with website content.

Most crawlers also follow rules set by the website owner in a special file called robots.txt. This file acts like a guide for crawlers, telling them which parts of the site they’re allowed to visit and which areas they should avoid. It’s a simple way for site owners to control how their content is indexed and accessed.

Data scraping

Data scraping is all about extracting specific information. Unlike crawlers, which want to discover and map out content broadly, scrapers focus on pulling out particular details, like product prices, contact information, or reviews.

This practice can be controversial. Many websites discourage or block scraping because it can put a heavy load on servers without offering anything in return. Too many automated requests can slow things down for regular users.

If you’re planning to scrape data responsibly, it's important to review the site's terms of service or API documentation to see if access is allowed and under what conditions. Many sites offer APIs specifically designed for structured, approved data access, helping avoid unnecessary strain and potential legal risks.

Hacking

Hacking is a completely different category. It’s about unauthorized access to computer systems, networks, or data that’s meant to be private or protected.

Hackers bypass security measures to steal sensitive information, cause harm, or disrupt services. This activity is clearly illegal.

Unlike scraping, which typically targets data that’s already public, hacking deliberately breaks through protections to get at private data.

Is data scraping legal?

The legality of data scraping isn’t simple. It depends heavily on where you are, what data you’re collecting, and how you’re doing it. Neither data scraping nor data mining is automatically illegal on its own, but there are plenty of ways it can cross legal lines.

When scraping can be allowed

Scraping publicly available, non-personal data is generally seen as lower risk. For example, researchers, journalists, and businesses often use scraping to collect price comparisons, monitor competitors, track online services, or study public online conversations.

In the EU, non-personal data usually carries fewer restrictions, provided you respect the site's terms of service and don’t undermine its business model.

In some cases, especially for scientific research, the law even creates carve-outs. The EU’s Digital Single Market Directive allows registered research institutions and cultural heritage organizations to conduct text and data mining on content they can lawfully access.

Where legal risks come in

Even if the data is public, scraping isn’t automatically free to use however you like. Here are the main legal issues highlighted by the sources:

• Terms of service violation: Some websites have terms of service that explicitly prohibit scraping. Ignoring those terms can lead to breach-of-contract claims. Courts in the EU have enforced these clauses (for example, the Ryanair vs. PR Aviation case), making clear that even unprotected data can be contractually off-limits.
• Database rights: In the EU, databases can have a special legal protection if they required substantial investment to create. Scraping large portions of such a database can infringe these rights, especially if it threatens the site’s business model.
• Copyright infringement: Data scraping can infringe copyrights if you extract and reuse copyrighted material in unauthorized ways. There are limited exceptions (like text and data mining for non-commercial research in the UK), but these require lawful access and are narrowly defined.
• Computer misuse: Accessing data in ways that violate a site’s technical barriers or terms could fall under cybercrime laws. For example, overloading a website with scraping requests can mimic a denial-of-service attack, potentially triggering criminal liability.
• Data protection and privacy laws: If the scraping involves personal data (anything that can identify a person), you must comply with privacy laws like the UK GDPR or EU GDPR. Even public personal data is protected, and controllers have duties around transparency, lawful basis, and security. Pseudonymizing data isn’t enough to avoid these rules if re-identification remains possible.

How businesses use data scraping today

Businesses use data scraping to gather valuable insights, streamline operations, and stay competitive in fast-moving markets. It’s a practical tool for collecting large volumes of publicly available data quickly and turning it into actionable information.

Competitive price monitoring

Companies like e-commerce retailers, airlines, and hotel chains often use scraping to track competitors’ prices in real time. This enables them to adjust their own pricing dynamically, offer better deals, and respond quickly to market changes. For example, a travel aggregator might scrape hundreds of airline websites to show users the cheapest flights available.

Market research and lead generation

Scraping helps businesses understand their markets and find new customers. By analyzing large volumes of public data (from social media posts and reviews to online directories), they can spot trends, identify customer needs, and collect leads for targeted marketing efforts.

Content aggregation

Scraping is also used to collect information from many sources and present it in one place. News aggregators, for instance, compile articles from various sites to create a unified feed. Similarly, comparison shopping platforms scrape product details from multiple retailers to help customers compare options easily.

Can data scraping harm website owners?

While data scraping offers value to those collecting data, it can also create real challenges for the websites being scraped. For site owners, these activities aren’t always harmless:

• Performance and infrastructure strain: Heavy or poorly managed scraping can flood servers with repeated automated requests. This can degrade site performance, slow load times for legitimate users, or even cause outages. For many businesses, these disruptions risk damaging their reputation and eroding customer trust.
• Operational costs and resource allocation: Handling high volumes of automated traffic can increase bandwidth usage and hosting costs. Sites may have to invest in stronger infrastructure or defensive measures to mitigate scraping, adding operational complexity and expense.
• Protecting proprietary and commercial interests: Scraping can undermine a website’s business model, especially when competitors harvest pricing, listings, or structured data to gain an advantage without permission or licensing. For many businesses, preventing large-scale automated copying is critical to maintaining fair competition and protecting their investment in building and maintaining the data.
• Enforcement challenges: Because scraping often comes from distributed IPs or anonymized traffic, detecting and blocking it can be difficult. Website owners may have to rely on technical measures and pursue legal options when scraping violates their terms.

How to protect your website from scraping

While no anti-scraping method is foolproof, using a mix of proactive measures can help deter scrapers and reduce potential harm. Here are some key strategies websites often use:

CAPTCHAs and rate limiting

CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) are widely used to block automated scraping tools. They work by presenting challenges that are easy for humans (like recognizing distorted text or selecting specific images) but difficult for bots. Adding CAPTCHAs to high-volume requests can significantly slow down or stop automated scrapers.

Rate limiting is another important tactic. By capping the number of requests allowed from a single IP address or user agent in a set timeframe, you can prevent bots from overwhelming your server with rapid-fire requests. When limits are exceeded, further requests can be blocked or throttled.

Bot detection tools and firewalls

Specialized bot management tools can help identify and block scraping bots without disrupting legitimate user activity. These systems use a mix of security rules, behavioral analysis, and machine learning to tell the difference between real visitors and automated scrapers.

Web application firewalls (WAFs) are another valuable layer of defense. A WAF monitors and filters traffic between your website and the internet, helping block common exploits, including techniques often used by scrapers.

Utilize obfuscation techniques

Another approach involves making your site's data harder to extract automatically. This can include obfuscating your HTML markup or network requests. For example, you might encode data server-side (using methods like Base64 or encryption) and decode it in the browser with JavaScript. This makes traffic harder to inspect and scrape directly.

Alternatively, obfuscated data can be embedded in the initial HTML and decoded client-side. This can frustrate simple HTML parsers by forcing them to understand your JavaScript logic.

However, these methods have trade-offs. They can be complex to maintain, often fail against advanced scrapers that run JavaScript, and may hurt site performance or usability for legitimate users, especially those with JavaScript disabled.