Everything you need to know about web scraping – explained without the jargon.

What Is Web Scraping?

Web scraping is the automated process of collecting data from websites and organizing it into a structured format like a spreadsheet or database. Instead of manually copying and pasting information from web pages, automated software – called a scraper – visits websites, reads the underlying code, and extracts only the specific data you need. The result is clean, organized information you can use immediately for analysis, decision-making, or business operations.

Think of it this way: every website you visit is built from code that tells your browser what to display. A web scraper reads that same code, but instead of rendering a visual page, it pulls out the data points you care about – product prices, business contact details, customer reviews, job listings – and delivers them in a format you can actually work with.

The practice has moved firmly into the mainstream. The web scraping market reached an estimated $1 billion in 2025 and is projected to grow to roughly $2 billion by 2030, expanding at a compound annual growth rate of around 14%. Businesses in e-commerce, finance, real estate, travel, and dozens of other industries now depend on scraped data for competitive intelligence, pricing strategy, and market research.

How Does Web Scraping Work? (The Simple Version)

You do not need a computer science degree to understand how web scraping works. The process follows a straightforward sequence that mirrors what you do every time you browse the internet – just faster and at a much larger scale.

Step 1: The scraper visits a web page. Just like your browser, the scraper sends a request to a website’s server asking to see a page. The server responds by sending back the page’s code – the HTML that contains all the text, images, and structure you see on screen.

Step 2: It reads the code. Rather than rendering a visual page, the scraper parses the HTML to understand the page’s structure: where the headings are, where the prices sit, where the contact information lives. Think of it as reading the blueprint of a building instead of walking through the finished rooms.

Step 3: It extracts the data you want. Using predefined rules, the scraper locates and pulls out only the specific data points you have requested – ignoring navigation menus, advertisements, and everything else that is not relevant.

Step 4: It cleans and organizes the results. Raw extracted data often includes formatting inconsistencies or extra characters. The scraper cleans this up and exports the results in a structured format – typically a spreadsheet (CSV or Excel), a database, or a JSON file that integrates with other software.

Step 5: It repeats at scale. The real power of scraping is repetition. A scraper can visit thousands or even millions of pages, performing this same process automatically. What would take a person weeks of manual copying takes a scraper minutes or hours.

A Simple Analogy: The Research Assistant

Imagine you need to compare prices for a specific product across 500 different online stores. You could open each store’s website, find the product, write down the price, and move to the next one. After days of work, you would have a spreadsheet of 500 prices.

A web scraper is like hiring a tireless research assistant who can visit all 500 stores simultaneously, record every price instantly, and hand you a finished comparison spreadsheet before your morning coffee gets cold. The assistant does not get distracted, does not make typos, and can repeat the same task tomorrow to track how prices have changed.

That is web scraping in essence: automated data collection that replaces tedious manual work with speed and precision.

What Kind of Data Can You Scrape?

Virtually any information that is publicly visible on a website can be scraped. The most common types of data businesses extract include:

Web Scraping vs. APIs: What Is the Difference?

You may have heard the term API (Application Programming Interface) mentioned alongside web scraping. Both are methods for getting data from websites, but they work quite differently.

An API is like a formal front door. The website’s owner builds a structured way for you to request specific data. You ask for exactly what you need, and the API sends it back in a clean format. APIs are reliable and efficient – when they exist. The problem is that most websites either do not offer APIs, restrict access to certain data, or charge significant fees for usage.

Web scraping is more like reading information from a public bulletin board. The data is already visible to anyone who visits the website. A scraper simply automates the process of reading and recording that publicly available information. Web scraping fills the gap when APIs are unavailable, too limited, or too expensive.

Many businesses use both approaches in combination: APIs for their own internal systems and web scraping for gathering competitive intelligence from external sources.

Who Uses Web Scraping?

Web scraping is no longer a niche activity reserved for software engineers. Today, it spans virtually every industry. E-commerce and retail represent the largest segment, accounting for roughly 37% of the web scraping market as of 2024. Financial services, real estate, travel, healthcare, and recruiting are all significant users as well.

Here are some of the people who rely on web scraping daily:

E-commerce managers track competitor pricing across hundreds of products and marketplaces, adjusting their own prices dynamically. According to industry data, approximately 81% of US retailers now use automated price scraping for dynamic repricing strategies.

Sales and marketing teams build prospect lists by collecting publicly available business contact information from directories, industry sites, and professional networks. Scraped lead data can cost a fraction of what pre-built data providers charge – often $0.01 to $0.10 per record compared to $0.10 to $1.00 or more from commercial databases.

Market researchers gather consumer sentiment from product reviews, track industry trends, and monitor competitor activity at a scale that would be impossible manually.

Real estate professionals monitor property listings, track price movements, and analyze market conditions across multiple listing platforms simultaneously.

Financial analysts collect alternative data – earnings reports, news sentiment, pricing trends – to inform investment decisions. The alternative data ecosystem that supports hedge funds and analytics is estimated at $4.9 billion.

AI and machine learning teams use web scraping to build training datasets. Over 65% of organizations now use web scraping to build domain-specific datasets for predictive analytics and large language models.

Is Web Scraping Legal?

This is one of the most common questions about web scraping, and the short answer is: scraping publicly available data is generally legal in most jurisdictions. Courts in the United States have consistently ruled that publicly accessible information on the open web can be collected through automated means.

However, legal considerations do apply. Important boundaries include:

Publicly available data is generally fair game. Product prices, business listings, published news articles, and other information that anyone can view without logging in are typically considered scrapable.

Terms of service matter. Some websites explicitly prohibit scraping in their terms of service. While enforcement varies, it is wise to be aware of these restrictions.

Personal data requires caution. Regulations like GDPR in Europe and CCPA in California impose strict rules on collecting, storing, and processing personal information. Scraping personal data – email addresses, phone numbers, individual profiles – requires careful compliance with applicable privacy laws.

Authentication barriers signal restriction. Data behind login walls, paywalls, or other access controls is generally off-limits for scraping without explicit permission.

For businesses that need data but want to avoid legal complexity, working with a managed scraping service that handles compliance is often the safest approach.

Common Challenges with Web Scraping

While the concept of web scraping is simple, executing it reliably at scale involves real challenges. Understanding these obstacles helps explain why many businesses ultimately choose to outsource rather than build in-house.

Anti-bot protections. Websites increasingly deploy CAPTCHAs, IP blocking, rate limiting, and device fingerprinting to prevent automated access. Research suggests that approximately 72% of scraping attempts are blocked by anti-bot measures on modern websites.

Website structure changes. Websites update their layouts frequently. When a site’s HTML structure changes, a scraper built for the old layout breaks. Maintaining scrapers requires ongoing technical attention – a cost that compounds as you scrape more sources.

Data quality. Raw scraped data often contains duplicates, formatting inconsistencies, missing fields, and errors. Without rigorous cleaning and validation, poor data quality can lead to flawed decisions. Gartner estimates that poor data quality costs organizations an average of $12.9 million per year.

Scale and infrastructure. Scraping a handful of pages is straightforward. Scraping millions of pages across hundreds of websites requires proxy management, distributed infrastructure, and significant technical expertise.

Legal compliance. Navigating the patchwork of international data privacy regulations – GDPR, CCPA, and others – adds another layer of complexity, especially for businesses operating across borders.

A Simpler Approach: Managed Data Scraping

For many businesses, the most practical path is not building or managing scrapers at all. Managed data scraping services handle the entire workflow – from scraper setup and anti-bot circumvention to data cleaning and delivery – so you can focus on using the data rather than collecting it.

Tendem takes this further by combining AI automation with human expert verification. AI handles the speed and scale of data extraction, while human co-pilots validate accuracy, handle edge cases, and ensure the delivered data actually matches your business requirements. The result is clean, verified data without the technical overhead of maintaining scraping infrastructure yourself.

Whether you need competitor pricing data, lead lists, market research, or any other publicly available information, a managed approach eliminates the learning curve and lets you go from data need to delivered results in days rather than weeks.

Web Scraping vs. Manual Data Collection

How to Get Started with Web Scraping

If you are new to web scraping, you have three main paths depending on your technical comfort level and the scale of data you need:

No-code scraping tools are the simplest entry point. Platforms like Browse AI, Octoparse, and others let you point and click on the data you want to extract without writing any code. These work well for small, occasional scraping tasks.

Custom code offers full flexibility for technical teams. Python libraries such as Beautiful Soup, Scrapy, and Playwright are the most popular tools, used by roughly 70% of scraping practitioners. This approach requires programming skills and ongoing maintenance but gives complete control over the process.

Managed scraping services handle everything for you. You specify what data you need, and the service takes care of scraper development, anti-bot management, data cleaning, compliance, and delivery. This is the fastest path to clean data for teams without dedicated scraping expertise.

The right choice depends on your volume, frequency, and internal capabilities. For one-off projects with simple requirements, a no-code tool may suffice. For ongoing, large-scale data needs where accuracy matters, a managed service typically delivers better results with less overhead.

Key Takeaways

Web scraping is simply the automated collection of data from websites – replacing manual copy-paste work with software that is faster, more consistent, and infinitely more scalable. It powers pricing intelligence, lead generation, market research, and AI development across virtually every industry.

The market is growing rapidly because businesses that use data to inform decisions consistently outperform those that rely on intuition alone. Whether you scrape data yourself, use no-code tools, or partner with a managed service, the underlying principle is the same: the information you need is already on the web. Scraping just helps you access it efficiently.

If you are ready to explore what web scraping can do for your business, Tendem’s AI + Human data scraping service delivers clean, verified data without the technical complexity. Share your data requirements, and get structured results delivered to your inbox.