What is web scraping and how does it work?

What is web scraping?

Web scraping is the process of automatically extracting data from websites. When people need to gather information from the web at scale, they can use an automated tool called a web scraper to gather the information for them, which is much faster and more efficient.

Although you would typically use a web scraper to collect large amounts of data, if you have ever copied-and-pasted information from a website, you have performed the same function as a web scraper on a smaller, more manual scale.

We use the word “scraping” to describe this data extraction process because you are metaphorically “scraping” the data off a website the same way you would physically scrape something to extract a specific part of it.

Some of the most common uses for web scraping include ecommerce intelligence, lead generation, brand monitoring, and market research, although the applications for web scraping are virtually limitless.

How does web scraping work?

Web scraping uses software designed for data extraction from websites. These tools are called web scrapers, and they automate the scraping process so you don’t have to manually visit each website you want to scrape and then find and copy the data yourself.

Web scraping uses a combination of crawlers and scrapers to get the data you need. The web crawler browses the target websites and indexes their content while web scrapers quickly extract the information you have requested. You can specify the file format that you’d like the web scraper to return the results in and which storage location you’d like the web scraper to save your data to.

What is the difference between web crawling and web scraping? →

Web scraping process

The first step of web scraping uses a web crawler to connect to the target website that contains the data we need. This can be straightforward or very complex, depending on how the website protects its data from scraping.

• You may need to use a proxy service at this stage, to give you a unique public IP address with specific characteristics that will help you to avoid the website’s bot-blocking measures.
• If the target website uses dynamic content that relies heavily on JavaScript execution, you may also need to use a scriptable web browser (that has all the capabilities and features of a standard web browser but allows scripts to interact with its functionality).
• At this stage, you may also need to change several fingerprints (e.g. TCP fingerprints, TLS fingerprints, or web browser fingerprint randomizers) to make the web scraping client appear unique and consistent. This makes it less detectable when accessing websites.

Once the crawler has connected to the target website, it will retrieve the entire contents of the web page, structured in HTML. This format is optimized for machine processing rather than human reading. Web scrapers use different techniques and tools to extract the specific information you are looking for from the HTML content.

• There are lots of libraries you can use for this task, and they use different approaches and languages, such as XPath, CSS selectors, or regular expressions (Regex).
• By using these libraries and methods, web scrapers can programmatically navigate through the HTML structure, target specific elements, and extract the relevant data fields or content.

In the real world, you will not scrape data just for the sake of scraping. You will need to prepare your data to help you meet your business’ end goals. Now that the data has been scraped from the HTML content, you could choose to apply a number of typical data engineering routines to ensure your information is clean and properly formatted.

• Cleaning data: Remove irrelevant or incorrect results
• Validating data: Ensure that the data is accurate
• Correcting data: Fix errors where possible
• Enriching data: Combine data from different sources
• Formatting data: Adapt the data structure to meet your business and storage needs
• Storing data: Save your data in your preferred format

If you feel ready to start scraping a real website, we have published an in-depth step-by-step guide on web scraping with Python. This guide is suitable for beginners, although some knowledge of using Python will come in handy.

What is web scraping used for?

There is virtually no limit on the applications for web scraping. Many businesses rely on bulk data to inform their strategies, and some businesses even scrape data for the purpose of repackaging and selling it. 

For example, a company that sells SEO tools might scrape search engine results pages (SERPs) to find out what position different websites rank in for individual keywords. They can then format this data and resell it to businesses who want to improve their search engine rankings.

Some of the most common web scraping use cases are:

• Ecommerce price tracking
• Search engine optimization (SEO)
• Market research
• Lead generation
• Social media marketing
• Brand monitoring

Web scraping examples

Scraping ecommerce data

Imagine you're running an online store that sells sneakers. Web scraping can help you to monitor your competitors' prices to make sure yours are competitive.

You can create or purchase a web scraping tool (called a sneaker bot) that automatically checks the prices of similar sneakers on other websites. The scraper visits those websites, grabs the prices, and brings them back to you. Then, you can analyze this data to see if you need to adjust your prices in line with the market averages.

Let's say you scrape data from three popular sneaker websites every day. Your web scraper collects the prices of the specific sneaker models you sell. After a week, you notice that one of your competitors is consistently selling a particular model at a lower price. Armed with this information, you can decide to either match their price or adjust your marketing strategy.

In this case, web scraping helps you stay competitive by giving you real-time information about what others are charging for similar products. It's a way to ensure you understand the competitive landscape without spending lots of time manually checking each website.

Web scraping API for ecommerce →

Web scraping for search engine optimization (SEO)

You can use a web scraper on search engines just as you can with other websites. Search engines have a lot of fields that can be scraped - for example, you could scrape all the meta titles of a search engine’s results page (SERP) or find all the URLs of the top-ranking results. You can even scrape search engines for information on their image results.

Imagine you're managing the SEO for a travel website that offers vacation packages. You want to find out which websites are ranking well on Google for travel-related keywords. This data will help you understand what your competitors are doing and how you can improve your travel website and make it more visible to potential customers.

You can use a web scraping tool for Google Search to automatically gather data on which websites appear at the top of search engine rankings for important travel-related keywords. You can analyze which websites consistently rank highly and mimic their tactics on your own website.

For example, if you find that some competitors are excelling in specific keyword categories, you can adjust your content strategy to better target those keywords. You can also optimize meta tags, improve backlink profiles, or refine other aspects of your SEO strategy based on what you learn from competitor rankings.

Scraper API for search engines →

Scraping social media platforms

You can use web scrapers to extract public information from any of the most popular social media platforms. For example, at SOAX, we have scraper APIs for TikTok, Instagram, Facebook, Snapchat, X, Reddit, and LinkedIn – and we are always adding more. You can use the data from these social media platforms to monitor brand mentions and reputation, track competitors’ social media performance, or to find the most popular trends and hashtags.

Imagine you’re managing social media marketing for a fashion brand, and you want to know what types of posts and what kinds of influencers receive the most likes and comments from your target audience. This data can help you make the most efficient use of your social media and affiliate marketing budgets, and improve your engagement metrics.

You could develop or purchase a web scraper to automatically extract data from Instagram, and then use the data to analyze what types of posts and what kind of influencers receive the most engagement from your target audience. 

In this example, a sophisticated web scraper could return multiple metrics, such as the number of comments and likes that a post received, and who posted it. You could even use a web scraper to return information about the post itself – for example, whether the post was a still image or a video, and whether it was posted to Instagram Stories, Reels, or to a user’s feed.

Scraper API for social media →

Is web scraping legal?

Web scraping itself is not illegal. It's a tool, much like a web browser, that can be used for both legitimate and illegitimate purposes. The legality hinges on how you scrape and the type of data you gather.

Web scraping has several legal gray areas that depend on your specific circumstances, the nature of the data you want to scrape, and how you decide to use it.

Terms of service

Many websites have terms of service that explicitly prohibit scraping. Violating these terms can lead to legal action, such as breach of contract lawsuits. However, recent cases like Meta vs. Bright Data have highlighted that even violating terms of service doesn't automatically make scraping illegal. Courts are increasingly considering factors like the type of data scraped and the scraper's intent.

Meta vs. Bright Data case

A landmark ruling in 2023 saw Meta (formerly Facebook) lose a legal battle against Bright Data, a web scraping company. This case set a significant precedent, suggesting that scraping publicly accessible data, even from behind a login, may not always violate terms of service. This ruling doesn't give scrapers carte blanche, but it does highlight the evolving legal landscape around web scraping.

Protected content

Scraping copyrighted or otherwise protected content can infringe on intellectual property rights. This could result in legal action from copyright holders, potentially leading to fines or other penalties. 

Intellectual property rights generally apply to creative works like articles, photographs, or software code, so you should not scrape these kinds of content without explicit permission from the copyright holder. To avoid potential legal issues, focus your scraping efforts on publicly available data (for example, facts, figures, or other non-creative expressions) and always respect intellectual property rights.

Data behind a login wall

It is sometimes illegal to scrape data that requires you to be logged in to your target website. 

While the Computer Fraud and Abuse Act (CFAA) in the US and similar laws elsewhere may apply when you scrape data that you can only access when logged in, recent court rulings suggest that this isn't always a straightforward violation. A federal judge in California ruled that the scraping of publicly available data from behind a login wall was acceptable in the case of Meta vs. Bright Data.

This doesn't mean that all scraping behind a login is legal, but it does highlight a shift in the legal landscape towards recognizing the importance of accessing public information, even if it requires logging into a platform.

What tools do you need to scrape the web?

The tools you will need for your web scraping project can vary depending on the task, the amount of data you need to scrape, and your available resources.

• You can use a proxy service like SOAX to get unique IP addresses and rotate them as needed.
• You may need a scriptable web browser and a library to control it. You can use a library like Chrome with Playwright, Puppeteer, or Selenium to automate and control your web browser’s actions.
• If your web scraping activities need increased stealth to evade bot-blocking methods, you can use an antidetect browser to conceal or alter various identifying characteristics of your traffic.
• You can use open-source libraries to hide browser scripting and create unique fingerprints to avoid detection.
• You will need to decide on a suitable runtime environment (e.g. a computer or a service such as ScrapeOps). This can just be your personal laptop at first.
• You will need a database to store your scraped results. This will make it easy to retry your failed requests, and organize your data for analysis or processing.