Turn webpages into LLM-ready data at scale with a simple API call
Your no-nonsense guide to scraping the web.
Web scraping, or web data extraction, is the process of extracting publicly available web data using automated tools – web scrapers. This information is usually returned in a structured format and used for repurposing or analysis. Businesses use this data to build new solutions like apps and websites or make better decisions based on patterns and insights from the collected data.
In this article, we’ll explore:
Web scraping is a process where data, or any essential piece of information, is automatically collected from a website for the purpose of enabling humans, programs, or AI models to make more informed decisions.
Some popular websites, such as StackOverflow, have API endpoints for analysts or engineers to get structured data (e.g., Excel or JSON) extracted easily. But, this is not the case for every website, and this is where web scraping becomes more important.
Most of the time, a large chunk of data is needed while doing research. However, it can be extremely tedious to fish and extract these pieces of information one after the other manually.
Another option is to screenshot data, but the downsides of that include the inability to structure data or feed it into programs. For this reason, it is unarguably better and more efficient to scrape data from websites.
The automation aspect of web scraping is introduced with the use of programs—which can be developed in Python, JavaScript, or any other languages—to automatically search the web for the provided URL and extract the data there.
Professionally, scraping data is not enough; it should also come in a structured format and be in a separate file; all these can be well-defined in the program.
That said, the success of web scraping is not only dependent on writing programs, as various websites have many sophisticated bot-bouncing measures in place. This is the reason hyper-efficient web scraping APIs, such as ScraperAPI, become an important weapon in any web scraper’s arsenal.
Web scraping tools or web scrapers are software solutions designed specifically to access, scrape, and organize hundreds, thousands, or even millions of data points from websites and APIs and export the data into a useful format – typically a CSV or JSON file.
These tools can either be out-of-the-box solutions (pre-built scrapers) or scripts built using a programming language and their corresponding scraping libraries (like Python’s Beautiful Soup or JavaScript’s Cheerios.
A web scraper is an automated tool that extracts data from a list of URLs based on pre-set criteria. They are used to gather information to find names, prices, addresses, and other data, then export that information into a usable format like a spreadsheet or a database.
They’re used for use cases like finding property listings for real estate, conducting market research, gathering intelligence on competitors, etc.
There are two main components to a web scraper, the web crawler and the scraper itself:
Let’s say that you want to create a Twitter bot that publishes quotes from humanity’s greatest minds. You could definitely read a lot of books and manually create a database with all the quotes your bot will use, or better yet, you can scrape all the phrases from different websites and automate the whole process.
https://quotes.toscrape.com/ using Python and Beautiful Soup:
import requests
from bs4 import BeautifulSoup
url = 'https://quotes.toscrape.com/'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
all_quotes = soup.find_all('div', class_= 'quote')
for quote in all_quotes:
quote_text =
quote.find('span', class_= 'text').text
print(quote_text)
Note: If you’d like to learn how to build a scraper from scratch, check out our Python web scraping tutorial for beginners or take one of the best web scraping courses for Python and JavaScript
Of course, this is just a form web scrapers can take.
In other scenarios, you might want to use technologies like Selenium to control a headless browser and scrape dynamic content. In others, using a scraping API like ScraperAPI would let you automate data collection or even extract data in real-time from platforms like Amazon.
After understanding what web scraping is all about, it is brilliant to ask what exactly it is used for and if you can also find it useful in your business endeavors. For clarity, here are current use cases of web scraping in several domains:
Large Language Models (LLMs), such as ChatGPT and Claude, are only as intelligent as the dataset they were trained on; the larger and more diverse the data, the better. Hence, AI and machine learning companies often spend more time and resources scraping data to train their LLMs.
For example, if a model is to be trained on debugging, relevant questions on it across StackOverflow, StackExchange, and Reddit must be properly scraped, analyzed, and fed into the dataset.
Once the dataset is robust enough, the intelligence of the LLM will be more impressive.
Businesses that want to keep growing must retain their users and always be on the hunt for more; this is what lead generation is all about.
With web scraping, businesses are able to get the work email of their target audience and try to seal partnerships or close sales with them. This data can be extracted from publicly available platforms.
With a simple scraping program, a business can get the details of thousands of leads. This would have been extremely difficult and time-consuming if attempted to be done manually.
Humans are emotional beings, and a good number of their decisions are based on emotions. This can be a source of truth for aggregating possible outcomes of events.
For example, an insurance company might want to know what the people in a location feel about insurance generally. It can create a program and scrape tweets mentioning that keyword within a particular time frame in such a location.
After extracting these data, the company might visualize the data to know the state of sentiment about its services. This also applies to other industries.
The way to stay at the top in business is to often take a few steps back to review, research, and analyze. Another way to speed up this research is to see what customers are buying from your competitors and why they choose them over you.
Web scraping can help businesses make data-driven decisions, such as allowing reviews and prices of goods to be extracted from competitors’ websites.
This way, business leaders can spot:
Some companies invest in various asset classes, such as bonds, stocks, and crypto. Therefore, it is imperative to monitor how these assets are performing from time to time; this will be helpful in spotting market trends and making apt financial decisions accordingly.
For convenience, scraping scripts can be run to alert when your asset hits certain figures on the chart, which is easier than always checking the chart manually.
It is also the case that some hedge funds and trading companies run scraping programs to spot arbitrage opportunities for some assets.
Web scrapers can be differentiated based on who built them. There are a couple of options:
Anyone with technical knowledge can write a web scraping program. Bear in mind that sometimes, basic engineering skills and knowledge of how the web works won’t be enough.
Hence, there is a need to be more sophisticated in development to write complex programs and bypass high-level anti-scraping agents.
In this case, the web scraping program is already written and can be customized by whoever wants to run it.
For example, a whitelabel scraper might have been written for LinkedIn pages, only that whoever wants to run it must provide a targeted link as well as other details. This can be more helpful for non-technical business leaders or for tech teams to build on top of it.
Web scraping extensions are simple programs (or add-ons) installed on top of your browser. These extensions use your browser’s capabilities to extract extra data from the sites you’re visiting.
The major advantage of these extensions is that they can collect dynamic data using your browser’s rendering engine, making it easier to collect data from JavaScript-heavy sites.
However, advanced features like IP rotation and CAPTCHA handling can’t be implemented because these applications live on your browser, so these are better suited for small scraping projects or to get sample data to pitch a project.
On the other hand, software-based web scrapers can live on your local machine or in the cloud, giving more flexibility and more advanced features necessary to collect data at scale.
You can also consider scraping APIs, like ScraperAPI, as software applications. These tools, unlike browser extensions, have more automation and scalability options and can be integrated into complex data pipelines.
Generally, the interface can be with a:
Web scraping is legal so far the extracted data are neither personal nor copyrighted. In most jurisdictions, web scraping is legal if the data is obtained in good faith, without causing harm, and utilized for a good cause. So make sure to check the site’s robots.txt file to ensure you’re not surpassing rate limits that could overwhelm its servers.
This is understandable, as public data can be needed for research and analyses.
In quite a recent lawsuit between Meta vs BrightData, the court dismissed the case and held Meta did not sufficiently prove that BrightData scraped nothing other than publicly available data.
Having said that, it is important to mention that web scraping can be considered illegal under two important conditions: personal data and copyrighted data.
According to the provisions of GDPR, personal data should not be scraped without consent. Similarly, once a website has copyrighted its content, they have secured the intellectual property of the data therein and, therefore, making it illegal to scrape.
On this note, motive and usage are two important factors that can determine whether or not web scraping is legal.
Read more: Is Web Scraping Legal? The Complete Guide
Yes, web scraping is still used by individuals who want to use data for personal reasons, businesses that want to make data-driven commercial decisions, and even AI companies that want to make their models more intelligent.
No, ChatGPT cannot scrape websites. It was designed as an LLM and not a web scraping API. Nonetheless, it can be helpful for analyzing datasets built using web scraping.