Web scraping is the process of automatically extracting information from websites and turning it into data that can be processed, stored, or analyzed. It can be used to collect information such as product prices, reviews, search results, articles, images, and other publicly available web content. Web scraping often involves several related technologies that perform different jobs. A web crawler can discover which pages need to be visited, while a web scraper extracts the required information from those pages. The resulting data can then be cleaned, validated, and organized into a dataset or delivered to another application.
Not every scraping job requires a crawler. If the URLs are already known, a scraper can access those pages directly. Crawling becomes especially useful when the collection system first needs to discover large numbers of pages across a website.
How does web scraping work?
A web scraping system needs to find or receive the pages it wants, access them, extract the required information, and turn the results into usable data. A simplified web scraping process looks like this:
| Stage | What happens | Example |
|---|---|---|
| 1. Discover (find the pages) | Identify the webpages that contain the information you need. A crawler can follow links to discover pages automatically, or the URLs can already be known. | A crawler discovers 40,000 product pages across an online retailer. |
| 2. Access (retrieve the content) | Connect to each page and retrieve its content. Depending on the website, this may involve a direct HTTP request, a browser, or access infrastructure. | The collection system requests each product page so its content can be read. |
| 3. Extract (pull the required fields) | Identify and pull the specific information required from the page. This is the job performed by the web scraper. | The scraper extracts the product name, price, SKU, rating, and availability. |
| 4. Process (clean and validate the data) | Clean, validate, deduplicate, and normalize the extracted information so records follow a consistent structure. | Prices are converted into a consistent format and duplicate product records are removed. |
| 5. Output (dataset, API, database, or application) | Store or deliver the processed web data in a form another system can use. | The finished records are delivered as a dataset, database, API response, or input to another application. |
A small scraping job may skip some of these steps. If someone already has 100 product URLs, for example, the scraper can go directly to those pages and extract the required fields. At a larger scale, the process becomes more involved. A collection system may continuously discover new pages, retrieve content from different geographic locations, extract millions of fields, retry failed requests, check the resulting data for errors, and deliver new records on a recurring schedule. For a deeper look at what changes when this process moves from a small scraper to production infrastructure, see Large-Scale Data Collection: Complete Guide.
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What kinds of data can be scraped from the web?
Web scraping is not limited to copying text from webpages. A scraper can collect many kinds of information as long as the required data is available through the source being accessed. What gets extracted depends on the website and the reason the data is being collected.
| Type of web data | What might be scraped | Example |
|---|---|---|
| Product data | Product names, SKUs, descriptions, specifications, categories, brands, and availability | A consumer electronics retailer tracks 50,000 competitor SKUs across five marketplaces to spot catalog gaps before a seasonal launch. |
| Pricing data | Current prices, previous prices, discounts, promotions, and shipping costs | A national retailer re-prices 200,000 products daily based on same-day competitor pricing pulled from a dozen marketplaces. |
| Reviews and ratings | Review text, star ratings, review dates, and other publicly visible feedback | A consumer goods brand pulls two million reviews across Amazon, Walmart, and Target listings each quarter to catch a defect trend before it shows up in return rates. |
| Search results | Rankings, result titles, snippets, URLs, ads, and other visible search information | An SEO team tracks ranking positions for 10,000 keywords across Google, Bing, and AI answer engines every week to catch visibility loss before traffic drops. |
| News and articles | Headlines, article text, authors, publication dates, URLs, and other article metadata | A financial services firm scans thousands of news sources daily for regulatory filings and competitor announcements that could move markets within hours. |
| Images and video | Media files or metadata such as titles, captions, tags, duration, and timestamps | An AI lab collects 500,000 product images and matching metadata to train a visual search model for ecommerce. |
| Business information | Locations, opening hours, contact details, categories, or other public business information | A franchise consultancy maps 30,000 competitor storefronts nationwide to identify underserved regions for expansion. |
A single scraping project can collect several of these categories at once. Scraping an ecommerce product page, for example, might produce product information, price, availability, rating, and review data from the same page. The important distinction is that web scraping describes how the information is collected, not one particular type of information.
Web scraping vs. screen scraping
Screen scraping extracts data from what is rendered on screen, the visual output of an application, rather than from the underlying HTML or page structure a web scraper normally reads. It was more common before modern websites exposed their content in a structured, machine-readable form, and it is still used today mainly against legacy systems, older desktop or terminal-based applications that never expose a clean data layer at all.
Web scraping reads a page’s underlying structure. Screen scraping reads whatever is displayed on top of it. For anything running as a normal modern website, web scraping is almost always the more reliable and more maintainable option, since it is working with the page’s actual data rather than an approximation of what a person would see on screen.
What is web scraping used for?
1. Price monitoring and ecommerce intelligence
Retailers and ecommerce teams can repeatedly collect prices, promotions, product availability, reviews, and catalog changes across marketplaces. For example, a company selling consumer electronics could monitor the same products across several competing stores each day. The scraper collects the underlying data, and an ecommerce intelligence system can then analyze it to identify price changes, assortment gaps, or competitor activity. See Ecommerce Intelligence: The Complete Guide for how collected web data becomes ecommerce intelligence.
2. Competitive intelligence
Companies can collect publicly available information about competitors, including product changes, pricing, advertising, positioning, and other market signals. Web scraping is the collection mechanism in this workflow. Competitive intelligence is what happens when that information is analyzed and used to understand competitors or make decisions.
3. AI training and data collection
AI teams can use web scraping as one method of collecting text, images, video, and other web data for datasets. The collection itself is only one part of that process. Data may also need to be validated, deduplicated, filtered, labeled, normalized, and reviewed for provenance and usage rights before it is suitable for a particular AI application. See Where to Get Training Data for AI for a broader look at AI training data sources.
4. Market and news monitoring
Organizations can scrape news sites, industry publications, public business pages, and other online sources to monitor changes over time. A market-intelligence team, for example, might collect headlines, publication dates, company mentions, and article URLs from hundreds of publications. That creates a continuously updated source of web data that can later be searched or analyzed for trends. News scraping is therefore not a separate technology. It is web scraping applied to news and article data.
Does web scraping work the same way on every website?
No. The basic goal is the same, access a page and extract the required information, but websites can deliver their content in very different ways. A simple webpage may return all of its useful information directly in its HTML. A scraper can request the page and extract the required fields without running a browser.
Other websites generate important content only after JavaScript runs. A product page might load its reviews, availability, or additional listings after the initial page response. Collecting that information may require a browser or another method capable of accessing dynamically generated content. The structure of the site matters too. A scraper collecting 100 known URLs has a very different discovery problem from one trying to find every product across thousands of category and pagination pages.
Some websites also vary what they return based on location, login state, device, or other request characteristics. That can make access itself part of the collection problem rather than simply the step before extraction. For the different ways a scraper can retrieve and interpret a page, see What Is a Web Scraper?.
Why can web scraping become difficult?
The basic idea behind web scraping is simple. Keeping a scraper working reliably across changing websites and large numbers of requests is harder. Common problems include:
- Website structure changes. A site redesign can move or rename the elements a scraper uses to identify data.
- Dynamic content. Important information may appear only after JavaScript runs or after a user interacts with the page.
- Rate limiting. Websites can slow or reject requests when automated traffic exceeds certain thresholds.
- Anti-bot systems. Sites may evaluate IP reputation, browser characteristics, request behavior, and other signals associated with automated traffic.
- Geographic differences. Prices, products, search results, and other content can vary depending on where a request originates.
- Inconsistent data. Missing fields, alternate templates, duplicate records, and formatting differences can make the extracted data unreliable if it is not processed correctly.
- Scale. A script that works for 100 pages may need a very different architecture when it has to collect from millions of pages continuously.
These problems also happen at different stages. A crawler can fail to discover a page. The collection system can fail to access it. The scraper can access the page but extract the wrong value. Or the correct value can be collected but processed incorrectly afterward. For the technical reasons automated requests are blocked, see Why Do Web Scrapers Get Blocked?.
Are there rules around web scraping?
Yes. Technical access and permitted use are related but separate questions. The fact that information can technically be accessed by a scraper does not by itself determine whether every method of collecting it or every downstream use of that information is permitted. Questions around web scraping can involve several different considerations:
| Consideration | Question to ask |
|---|---|
| Access | Is the information publicly accessible, or does accessing it require authentication, payment, or another restricted mechanism? |
| Website rules | Does the website publish terms or technical instructions that apply to automated access? |
| Type of data | Does the collection involve personal information, copyrighted material, or other data subject to additional protections? |
| Jurisdiction | Which laws apply to the organization collecting the data, the source, and the people or rights involved? |
| Intended use | What will happen to the data after it has been collected? |
| Rights and provenance | Where did the data come from, and what rights or restrictions may apply to its downstream use? |
This is why a question like “can news websites be scraped?” has two parts. Technically, a scraper may be capable of collecting a headline, publication date, author, article text, or URL from a news website. But paywalls, authentication, contractual terms, copyright, and the intended use of the resulting content can introduce separate considerations. The same distinction matters for AI training data. Being able to access content does not automatically establish that it can be used for every training or downstream purpose.
The dedicated [Is Web Scraping Legal?] guide covers these questions in more detail.
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FAQs
- What is web scraping? Web scraping is the automated process of extracting data from websites and converting it into a structured, usable format.
- What is scraping? “Scraping” is typically shorthand for web scraping, the same process of automatically pulling data from web pages.
- What is price scraping? Price scraping is web scraping focused specifically on collecting pricing data across retailers or marketplaces, usually for competitive monitoring or pricing strategy.
- How do you scrape a website? At a basic level, a scraper accesses a page, identifies the specific fields it needs, and extracts them into a structured format, typically after a crawler has already found the relevant pages. For a full walkthrough at production scale, see Large-Scale Data Collection: Complete Guide.
- Can you scrape Google Search results? Search result pages can technically be scraped, but Google actively restricts automated access to its search results and its terms of service place limits on this specific use case, which makes it a higher-risk target than scraping an ordinary website.
- Is web scraping the same as data mining? No. Web scraping is how data gets collected. Data mining is what happens once it has been collected, finding patterns, catching duplicates, and filtering out low-quality or synthetic content. See Web Scraping vs. Data Mining for the full breakdown.
