Top web scraping challenges have evolved dramatically from the simple days of parsing static HTML. I’ve been building scrapers for years, and let me tell you – even simple tasks have turned into a complex chess match between developers and websites. From sophisticated CAPTCHAs, to JavaScript, the obstacles continue to multiply.
In this article, I’ll break down the major hurdles you’ll face when scraping data in 2025 and show you how ScrapingBee can help you jump over these barriers without breaking a sweat. Whether you’re dealing with IP blocks, dynamic content, or legal concerns, there’s a solution that doesn’t involve spending weeks building complex infrastructure.
Quick Answer (TL;DR)
Let's start with the basics: What is web scraping? Generally speaking, it's a process of automatically extracting web data, including search results, with a web scraping tool. Yet, even for the most powerful web scrapers, the process can be challenging.
In 2025, an average target website is likely to be protected with strict anti-scraping measures. This means that common web scraping challenges require a solution that can bypass CAPTCHA, avoid IP blocks, and handle JavaScript.
That's why I'm using ScrapingBee. It's an all-in-one API that handles the heavy lifting without a complex technical setup. All you need to do is follow our easy tutorials and start scraping data.
Here’s how simple it is to make API calls to web pages with ScrapingBee:
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
response = client.get('https://example.com')
print(response.content) # Clean HTML with all the data you need
Now that you have a quick overview, let's dive into the challenges of automatic data collection.
Why Web Scraping Has Challenges
I remember my first web scrapers. They were made with a simple script that sent requests and parsed HTML. Getting structured data or handling different data formats was possible, but the process was extremely time-consuming.
Those days are long gone. Modern websites are fortresses designed to keep unwanted web crawlers out, and learning how to collect data from different websites without getting blocked has become increasingly complex.
The web scraping challenges we face today exist because every website owner has strong incentives to protect their data. E-commerce sites don’t want competitors doing market research for business intelligence. Social media platforms want to sell data access rather than give it away. And content sites want to prevent their articles from being republished elsewhere.
To defend against web scrapers, web pages employ multiple layers of protection:
Browser fingerprinting – They collect dozens of data points about your browser to create a unique “fingerprint” that can identify your scraper even if you use multiple IP addresses.
Behavioral analysis – Dynamic sites track how you navigate, how quickly you click, and even how you move your mouse to determine if you’re human.
Content delivery networks (CDNs) – Many websites use services like Cloudflare, which can detect and block unusual traffic patterns typical of scrapers.
Dynamic page elements – Owners frequently update their website's structure, such as class names, IDs, and DOM structures, and scrapers must adapt to such changes to continue working.
The web scraping process has evolved from a technical challenge to an arms race. For every detection method, developers create countermeasures, such as CAPTCHA solving services. Then, websites develop even stricter anti-scraping technologies.
This is why simple requests libraries often fail – they don’t behave like real browsers, and they can't scrape multiple websites at once. To extract data reliably in 2025, you need to know how to crawl a website without getting blocked and get a tool that can mimic human behavior.
CAPTCHAs and Bot Protection
If you’ve ever wondered how to bypass CAPTCHAs while scraping, you’re not alone. CAPTCHA has evolved from those squiggly text images into sophisticated systems that can detect bots with incredible accuracy.
Modern CAPTCHA systems, such as Google’s reCAPTCHA v3, no longer display puzzles – they silently assess your behavior in the background and block you if you appear suspicious. Meanwhile, hCaptcha has gained popularity as an alternative that’s even harder for bots to crack.
The challenges with CAPTCHA include:
Invisible scoring – The monitoring systems work in the background; you don’t even know you’re being evaluated until you’re blocked.
Browser fingerprinting – They check if your browser has expected characteristics, including your user agent strings.
Behavioral analysis – They monitor how you interact with the page. For instance, it checks the request frequency, including AJAX calls (Asynchronous JavaScript and XML). Making multiple requests from a single IP address, especially rapid automated AJAX calls, is considered a bot behavior.
Machine learning models – They improve over time by learning from millions of interactions.
I once spent weeks trying to build web scrapers with a CAPTCHA solver or using CAPTCHA solving services, only to have them break due to a seemingly minor change in the CAPTCHA provider’s system.
Let's take a look at an example. Traditional approaches to solving CAPTCHAs include:
# This approach is likely to fail with modern CAPTCHAs
import requests
from bs4 import BeautifulSoup
response = requests.get('https://site-with-captcha.com')
soup = BeautifulSoup(response.content, 'html.parser')
# You'll probably see CAPTCHA elements in the HTML
Meanwhile, our API takes a different approach. Instead of trying to solve CAPTCHAs after they appear, it prevents them from appearing in the first place by using:
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
response = client.get(
'https://site-with-captcha.com',
params={
'premium_proxy': 'True' # Uses residential IPs that don't trigger CAPTCHAs
}
)
# No CAPTCHA in sight, just the data you need
Our approach is practical because it utilizes a network of residential IP addresses that appear legitimate to websites, combined with browser rendering that accurately mimics real user behavior. The system is constantly updated to stay ahead of new CAPTCHA technologies. Check out our article on how to bypass CAPTCHA while scraping for more tips.
IP Blocking and Rate Limits
IP bans are the most common obstacle in web scraping. Send too many requests from the same IP, and you’ll quickly find yourself staring at potential errors or, worse, silent IP bans where the site serves different content to your IP.
The web scraping issues related to IP blocking include:
Hard bans – Your IP is completely blocked, sometimes for months
Soft bans – You’re shown different content or fake data
Rate limiting – Your requests experience random delays or are throttled after exceeding a threshold
Geolocation restrictions – Content varies based on your IP’s location
Rotating proxies are essential for avoiding these issues, but managing your own proxies is a nightmare:
# Managing proxies manually is painful and unreliable
import requests
import random
proxies = [
{'http': 'http://proxy1:port'},
{'http': 'http://proxy2:port'},
{'http': 'http://proxy3:port'},
]
for _ in range(10):
proxy = random.choice(proxies)
try:
response = requests.get('https://target-site.com', proxies=proxy, timeout=5)
# Process response...
except:
# Handle proxy failure, retry with different proxy...
continue
This approach has multiple problems:
Free or cheap proxies are often already blocked
Proxy quality and reliability vary dramatically
You need constant monitoring and replacement of dead proxies
Managing proxy rotation adds complexity to your code
Our platform eliminates these issues with its built-in residential proxy management:
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# ScrapingBee handles proxy rotation automatically
response = client.get(
'https://target-site.com',
params={
'premium_proxy': 'True',
'country_code': 'us' # Optional: specify location if needed
}
)
These aren’t datacenter IPs that scream “I’m a bot” – they’re real residential proxies that look like regular users. Each rotating proxy is automatically switched based on the target website’s patterns and your scraping needs.
Handling JavaScript-Heavy Websites
Modern websites, especially those with elements such as login forms or comment sections, are built with frameworks like React, Vue, and Angular that load content dynamically after the initial page load. This headless browser scraping guide article will help you understand why traditional HTTP requests fail on these sites. Let's go through a quick overview.
When you visit a JavaScript-heavy site with a regular browser, here’s what happens:
The browser loads the initial HTML (often just a skeleton)
JavaScript files are downloaded and executed
The JavaScript makes API calls to fetch data
The DOM is updated with the fetched data
With a simple HTTP request, you only get step 1 – a nearly empty page with no useful data, and even when you do get content, organizing it into a usable data structure can be a challenge.
import requests
response = requests.get('https://javascript-heavy-site.com')
# Response contains minimal HTML with lots of JavaScript, but no actual data
print(response.text) # Probably shows loading spinners or empty containers
The web scraping techniques for handling JavaScript-heavy sites typically involve headless browsers like Puppeteer or Selenium, but these come with their own challenges:
Complex setup and maintenance
High resource usage
Frequent breakage with browser updates
Difficult to scale
Our platform is built to solve these issues:
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# ScrapingBee renders JavaScript automatically
response = client.get(
'https://javascript-heavy-site.com',
params={
'render_js': 'True', # This is actually on by default
'wait': 2000 # Wait 2 seconds for JavaScript to execute
}
)
# Response contains fully rendered HTML with all the data
It features a cloud-based, headless browser infrastructure that executes JavaScript in the same manner as a real browser. This means you get the fully rendered page with all the data in a structured format, without having to manage browser instances yourself.
For more complex scenarios, you can even execute custom JavaScript:
from scrapingbee import ScrapingBeeClient
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# Execute custom JavaScript to interact with the page
response = client.get(
'https://infinite-scroll-site.com',
params={
'render_js': 'True',
'js_snippet': '''
// Scroll down to load more content
window.scrollTo(0, document.body.scrollHeight);
// Wait and scroll again to load even more
setTimeout(() => {
window.scrollTo(0, document.body.scrollHeight);
}, 1000);
''',
'wait': 3000 # Wait for scrolling and content loading
}
)
This level of control means you can handle even the most complex JavaScript interactions without setting up your own browser infrastructure.
Legal and Ethical Questions
The web scraping issues related to legal and ethical considerations have become increasingly complex in 2025. While I’m not a lawyer (and this isn’t legal advice), here are the key concerns you should be aware of:
Robots.txt and Website's ToS
Most websites publish a robots.txt file that specifies which parts of the site can be crawled. Similarly, a website's terms (Terms of Service) often contain clauses about automated access. Violating these can potentially lead to legal issues.
Copyright and Database Rights
In many jurisdictions, scraping and republishing copyrighted content can lead to infringement claims. Some regions also have specific database rights that protect collections of information.
Personal Data and Privacy Laws
If you’re scraping personal data, you need to comply with regulations like GDPR in Europe, CCPA in California, and similar laws in other regions. These laws impose strict requirements on data collection and processing.
Recent Legal Developments
The legal landscape is constantly evolving. The hiQ vs. LinkedIn case established that scraping publicly available data isn’t a violation of the CFAA, but other cases have had different outcomes.
To minimize legal risks:
Respect robots.txt directives
Review and follow the Terms of Service where possible
Don’t overload servers with excessive requests
Be careful with personal data
Consider using official APIs when available
Consult with a legal professional for your specific case
Keep in mind that ScrapingBee helps with compliance by automatically respecting robots.txt, implementing proper request rate limiting, and providing features that reduce server and network bandwidth load. However, you’re still responsible for how you use and store data.
Build Reliable Scrapers With ScrapingBee
After dealing with all these web scraping challenges, I’ve found that our platform isn’t just another web scraping tool – it’s a complete solution that addresses every major obstacle we’ve discussed.
The platform combines multiple technologies into a single, easy-to-use API:
Premium proxy network – Access to residential IPs that don’t trigger blocks
Browser rendering – Full JavaScript execution without managing browser instances
CAPTCHA handling – Automatic prevention of CAPTCHAs
Smart request throttling – Intelligent rate limiting to avoid detection
Data extraction tools – Built-in selectors for easy data parsing
ScrapingBee Documentation provides comprehensive guides for common scraping scenarios and other techniques to help with specific challenges you might face. Sign up and get a free trial to test the service.
Example: Scraping E-commerce Prices
Let’s put everything together with a practical e-commerce web scraping example: scraping product prices from multiple web sources to compare prices and availability. This is a common use case that demonstrates the web scraping techniques we’ve discussed.
Here’s how to scrape product data using ScrapingBee:
from scrapingbee import ScrapingBeeClient
from bs4 import BeautifulSoup
import json
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# Target an e-commerce product listing page
url = 'https://example-ecommerce.com/products/category/electronics'
response = client.get(
url,
params={
'premium_proxy': 'True',
'render_js': 'True',
'wait': 2000 # Wait for dynamic content to load
}
)
# Parse the HTML response
soup = BeautifulSoup(response.content, 'html.parser')
# Extract product information
products = []
for product_element in soup.select('.product-item'): # Adjust selector for the target site
product = {
'name': product_element.select_one('.product-name').text.strip(),
'price': product_element.select_one('.product-price').text.strip(),
'rating': product_element.select_one('.product-rating').get('data-rating', 'N/A'),
'url': 'https://example-ecommerce.com' + product_element.select_one('a').get('href')
}
products.append(product)
# Save the results
with open('products.json', 'w') as f:
json.dump(products, f, indent=2)
print(f"Scraped {len(products)} products successfully!")
For more complex scenarios, you might want to scrape multiple pages or extract additional data. The platform makes it easy to scale your scraping operations without worrying about the underlying infrastructure.
Frequently Asked Questions (FAQs)
What makes web scraping difficult?
Web scraping challenges arise from websites’ anti-bot measures like CAPTCHAs, IP blocking, and complex JavaScript rendering. Sites constantly update their defenses, requiring scrapers to use sophisticated techniques like browser fingerprinting evasion and proxy rotation to extract data reliably.
How do you avoid getting blocked while scraping?
To avoid getting blocked, use rotating residential proxies, mimic human browsing patterns, respect rate limits, and render JavaScript properly. ScrapingBee handles these automatically with its premium proxy network and browser rendering capabilities, making it much easier to crawl websites without getting blocked.
Can ScrapingBee handle CAPTCHAs automatically?
Yes, ScrapingBee handles CAPTCHAs automatically by using residential IP addresses that don’t trigger these challenges in the first place. The system mimics real user behavior so effectively that most websites don’t even show CAPTCHAs to ScrapingBee requests.
Is web scraping legal everywhere?
Web scraping legality varies by jurisdiction and depends on what you’re scraping and how you use the data. Generally, scraping publicly available data is legal in many places, but you must respect robots.txt, terms of service, copyright laws, and privacy regulations. Always consult a legal professional for your specific case.
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.