Learn Everything About Web Scraping: Start Scraping Any Website

Web scraping is the process of automatically extracting publicly available information from websites and converting it into structured data.

In plain language:

Scraping turns the web into a spreadsheet.

Why this matter? Public web data is now one of the largest and fastest-growing sources of business intelligence.

But this isn’t just a theoretical guide.

Includes exclusive 2025 Data:

We analyzed billions of scraping tasks to show you exactly what data is being collected and the #1 reason why 35% of scraping projects fail.

Let’s dive in.

How Does Web Scraping Works

At a high level, web scraping consists of five steps:

Visit a webpage (like a browser)
Locate data (product titles, prices, contacts, etc.)
Extract text or attributes
Transform or clean data
Store data in a structured format

Every web scraper — from a simple browser extension to a distributed crawler hitting 500,000 pages an hour — follows the same conceptual pipeline:

1. Fetch a webpage

The scraper behaves like a browser or HTTP client.
Request sent → Page HTML returned.

2. Parse the document (DOM)

HTML becomes a structured tree.
Elements like <div>, <span>, <li> become selectable nodes.

3. Select target elements

Using:

CSS selectors
XPath
Pattern matching
Attribute filtering

Example:
//div[@class='product-title'] or .price span

4. Extract values

Text, numbers, links, attributes (src, href, alt), metadata, or JSON embedded in the page.

5. Format the output

Into:

CSV
Excel
JSON
SQL-ready tables
Direct-to-API pipelines

6. (Optional) Automate or scale

Scheduling, pagination, proxy rotation, anti-bot handling, error retries.

Web scraping looks simple on the surface, but the difference between hobby-level and enterprise-level scraping is massive. That’s where we’ll go deeper.

What Are The Most Common Uses of Web Scraping

Every industry runs on signals that do not exist in APIs:

E-commerce: price changes, stock status, discount validity
Travel: ticket volatility, fare classes, availability windows
Real estate: listing history, occupancy patterns
https://www.octoparse.com/template/categories/jobs& HR: job postings, skill demand, talent gaps
AI/ML: training data, labeled samples, document structures
Finance: sentiment, corporate disclosures, market intelligence

The internet did not standardize its data.
Web scraping is the universal adapter.

This is why:

Governments use scraping to monitor compliance
Researchers use scraping to track misinformation
Retailers use scraping to benchmark competitors
Brands use scraping to track successful resellling
AI companies use scraping to build datasets

Scraping exists because the web is the biggest database on Earth, but not a structured one.

How Many Types of Web Scrapers Are There?

TL;DR:

There are four main categories:

No-Code Web Scrapers: Tools like Octoparse let you extract data with clicks — no coding.

Browser Extensions: Lightweight scrapers like Chat4Data for single-page or small-volume tasks.

Programming Libraries: Python options (BeautifulSoup, Scrapy, Selenium). They are best for custom pipelines.

Enterprise Scraping Services

What About API?

What is API: Official data access channels provided by websites, designed specifically for extracting their data programmatically.

Best for:

Any situation where the website offers an API
Production systems requiring stability and legal clarity
Long-term data collection projects
Situations where you need official support

How they work: You register for an API key, read the documentation, send structured requests to API endpoints, and receive clean data in JSON or XML format.

Pros:

Legally clear and officially supported
Stable data format that rarely changes
Better performance (designed for data access)
Clear rate limits and usage terms
No risk of being blocked as a “bot”
Official documentation and support

Cons:

May cost money (freemium tiers, then paid plans)
Rate limits may be restrictive (100 requests/hour)
Doesn’t always provide all the data you need
Requires approval process for some APIs
May have strict usage restrictions

Common examples:

Twitter API: Access tweets, user profiles, trends
Google Maps API: Location data, reviews, business information
LinkedIn API: Professional profiles, company data (very restricted)
Amazon Product Advertising API: Product details, prices, reviews

When APIs aren’t enough: Many websites offer APIs but limit what data you can access or charge premium prices.

For example, LinkedIn’s official APIs expose only a small subset of profile fields (typically basic identity and current role) to most integrations and restrict storage, exporting, and combining of that data, even though the website shows far more detailed history, skills, and activity for the same profiles.

For more APIs in web scraping, check our article on the best web scraping APls for real time data.

1. No-Code Scrapers (Like Octoparse)

What they are: Visual tools where you point and click to select data. No programming needed.

Best for:

When no API exists or the API is too limited/expensive
Marketers, analysts, and researchers without coding skills
Getting data quickly (hours, not weeks)
Teams where multiple people need to run scrapers
Situations where pre-built templates exist for popular websites

How they work: You click on the data you want (like prices or phone numbers), the tool figures out how to extract it, and you export to Excel or CSV.

Pros:

Start scraping immediately without coding
Built-in features for common challenges (pagination, logins, CAPTCHAs)
Cloud scheduling (run scrapers automatically every day)
Templates for popular sites (Amazon, https://www.octoparse.com/template/collection/linkedin-scraper, https://www.octoparse.com/template/collection/yellow-pages-scraper)

2. Browser Extensions

What they are: Small add-ons for Chrome or Firefox that scrape data from pages you’re viewing.

Best for:

When no API exists and you only need data once
Quick, one-time data collection (50-200 items)
Testing if data is scrapable before building a full scraper
Personal research projects
People who need results right now without setup

How they work: Install the extension, click it while viewing a page, select what to extract, copy to clipboard or download as CSV.

Pros:

Free or very cheap
No account or installation required
Works instantly

Cons:

Limited to small amounts of data
No automation or scheduling
Most can’t handle anti-bot systems

Example tools: Chat4Data, Web Scraper (Chrome extension), Instant Data Scraper.

3. Programming Libraries

What they are: Code libraries in Python, JavaScript, or other languages that developers use to build custom scrapers.

Best for:

When APIs don’t exist and you need custom extraction logic
Developers comfortable writing and maintaining code
Projects requiring complex transformations during extraction
Integration with existing software systems
Situations where you need complete control

How they work: You write scripts that fetch pages, parse HTML, select elements, extract data, and save results—all in code.

Pros:

Complete control over every aspect of scraping
Free and open source
Can handle any level of complexity with enough effort
Integrates directly with data analysis tools (pandas, databases)
Can combine API calls with web scraping in same script

Cons:

Requires programming knowledge (Python, JavaScript, etc.)
Time-intensive to build and maintain
You handle all anti-bot issues yourself
No visual interface or templates

Popular options:

Python: BeautifulSoup, Scrapy, Selenium, Playwright
JavaScript: Puppeteer, Cheerio
Ruby: Nokogiri

4. Enterprise Web Scraping Services

What they are: Professional-grade services that handle massive scraping operations with guaranteed reliability.

Best for:

When APIs don’t exist or are prohibitively expensive at scale
Companies scraping millions of pages monthly
Mission-critical data pipelines (if scraping stops, business suffers)
Organizations needing global proxy networks
Teams with significant budgets ($5,000-$50,000+/month)

What they include:

Premium proxy networks (residential IPs that look like real users)
Professional support teams
Legal and compliance assistance
Infrastructure that handles massive scale
Service level agreements (guaranteed uptime)

Pros:

Handles scale most tools can’t reach
Professional support when things break
Advanced anti-bot capabilities

Cons:

Expensive (often $500-$5,000+ monthly minimum)
Overkill for small projects
Still requires technical knowledge

Example platforms: Octoparse, Bright Data, Oxylabs

How to Choose the Right Web Scraping Method

Start here: Does an official API exist?

✅ Yes, and it provides the data you need: Use the API (even if it costs money)
✅ Yes, but it’s too expensive or limited: Evaluate if web scraping is worth the trade-offs
❌ No API exists: Move to web scraping options below

Can you combine methods?

Yes! Many projects use APIs for some data and web scraping for additional information the API doesn’t provide.

Example: Use Twitter’s API to get tweet text and metadata, then scrape user profile pages for additional bio information the API doesn’t include.

Challenges in Web Scraping

Scraping is powerful, but not always simple. Here are the core challenges:

Anti-Bot Systems
Modern sites use aggressive blocking mechanisms.
Changing Website Structures
HTML changes break scrapers.
Data Quality Issues
Missing fields, inconsistent formatting, duplicates.
Scalability

Collecting small datasets is easy; millions of rows require strategy.

👉 For deeper explanations, read “10 Myths About Web Scraping”.

Best Practices for Effective Web Scraping

These are practical, universal, non-tool-specific guidelines.

Start with the smallest possible dataset

Validate structure early.

Build in resilience

Use XPaths/CSS selectors that don’t break.

Clean data upstream

Fix formatting at the extraction point.

Control your request rate

Respect server load to avoid blocks.

Automate the pipeline

Use scheduling, notifications, and auto-renew workflows.

What Octoparse Learned From 10+ Years of Scraping

What Are The Most Scraped Data Types

Top categories consistently include:

Product listings (e-commerce)
Reviews (Amazon, Yelp, hotel platforms)
Job postings
Real estate listings
Supplier & business directories
Academic / research metadata

These six categories represent roughly 68–74% of recurring extractions.

You may also be interested in:
1. How to Scrape eBay Listings Without Coding
2. How to Scrape Google Shopping for Price and Product Data
3. No-Coding Steps to Scrape Amazon Product Data