DOs and DON’Ts of Web Scraping in 2025

Here, I’ll walk you through the dos and don’ts of web scraping, helping you keep things running smoothly and get the most out of your data collection efforts. Let’s dive in!

Why Web Scraping Matters?

Before diving into the best practices of web scraping, it’s worth taking a moment to understand its significance. Web scraping enables the automated extraction of information from websites, and it’s used across a wide array of industries:

• E-commerce: Businesses scrape competitor pricing, stock levels, and product descriptions to stay competitive.
• Marketing: Marketers use scraping to monitor social media sentiment, news coverage, and industry trends.
• Research: Academic researchers scrape data for analysis, especially in fields like economics, social sciences, and political studies.
• Real Estate: Aggregating property listings, rental prices, and property features from multiple sources.

However, while scraping can provide massive benefits, it comes with technical challenges and ethical considerations. Improper scraping can cause servers to crash, violate terms of service, and result in legal repercussions. For this reason, it’s essential to approach scraping with caution and an understanding of best practices.

Essential Do’s for Successful Web Scraping

To set yourself up for success, it’s crucial to follow the key guidelines that can help you avoid common pitfalls, improve your scraper’s efficiency, and reduce the likelihood of getting blocked by your target website.

Rotate IPs to Avoid Detection

One of the simplest and most common anti-scraping mechanisms websites use is blocking IP addresses that generate too many requests within a short period. Over time, your scraper might get detected if you’re consistently making requests from the same IP, leading to an IP ban. To counter this, you should rotate IPs frequently.

This can be achieved using a proxy service. Proxies act as intermediaries between your scraper and the website, masking your real IP and allowing you to appear as though the requests are coming from different sources.

• Rotating IPs: With a rotating proxy, each request gets routed through a different IP address, significantly reducing the chances of getting blocked.
• Proxy Pools: Alternatively, you can maintain a pool of proxies and select one at random for each request.

Here’s a Python example of using proxies:

import requests
import random
urls = ["http://ident.me"] # List of URLs
proxy_list = [
"54.37.160.88:1080",
"18.222.22.12:3128",
# Add more proxy IPs
]
for url in urls:
proxy = random.choice(proxy_list)
proxies = {"http": f"http://{proxy}", "https": f"http://{proxy}"}
response = requests.get(url, proxies=proxies)
print(response.text)

While free proxies might seem attractive, they often come with reliability issues and can be short-lived. For long-term scraping, consider investing in paid proxies or proxy rotation services to ensure consistency and minimize downtime.

Rotate IPs to Avoid Detection with Bright Data Residential Proxies

One of the best ways to avoid detection while web scraping is by using Bright Data’s Residential Proxies. These proxies provide access to a vast pool of real residential IPs, making your requests appear as if they’re coming from real users. Unlike free proxies, Bright Data’s proxies are highly reliable, fast, and optimized for web scraping.

Why Use Residential Proxies?

✅ Automatic IP Rotation — Prevents detection and bans
✅ Access Geo-Restricted Content — Scrape data from any country
✅ High Success Rates — Works even on heavily protected websites

Example using Bright Data’s Residential Proxies in Python:

import requests

proxy = "http://username:[email protected]:22225"
proxies = {"http": proxy, "https": proxy}

response = requests.get("https://example.com", proxies=proxies)
print(response.text)

Bypass Anti-Bot Measures with Bright Data Web Unlocker

Many websites implement anti-bot measures such as CAPTCHAs, JavaScript challenges, and IP blocking. Instead of manually handling these issues, you can use Bright Data’s Web Unlocker, which automatically bypasses these restrictions and ensures a high success rate for your web scraping tasks.

Why Use Web Unlocker?

✅ Bypasses CAPTCHAs & Bot Protections — No manual solving required
✅ Handles Headers, Cookies & JavaScript Rendering — Ensures smooth scraping
✅ No Need for Manual Proxy Rotation — Works seamlessly with any target website

Example using Web Unlocker in Python:

import requests

proxy = "http://username:[email protected]:22225"
url = "https://example.com"

response = requests.get(url, proxies={"http": proxy, "https": proxy})
print(response.text)

Use Custom User-Agent Headers

Many websites check the User-Agent header to identify the type of device or browser making a request. If they detect requests coming from non-browser clients (like cURL or a Python script), they may block the request, thinking it’s a bot. A simple solution to this is to customize the User-Agent header.

The User-Agent string typically includes details about the browser, operating system, and device. By using a legitimate User-Agent from a modern browser, you can make your scraper appear more like a typical user.

Tip: It’s also a good idea to rotate User-Agent strings just as you rotate IPs, as sending the same one repeatedly can also raise flags.

Example:

user_agents = [
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
# Add more User-Agents
]
headers = {
"User-Agent": random.choice(user_agents)
}
response = requests.get("http://example.com", headers=headers)
print(response.text)

Investigate the Website’s Structure Before Scraping

Understanding the website’s structure is essential for an efficient scraper. Websites often provide structured data that’s easier to scrape, such as JSON responses from APIs or hidden input fields within the HTML. Before you start coding your scraper, take the time to explore the source code using browser developer tools (e.g., Chrome DevTools) to identify patterns, hidden fields, or structured data like Schema.org markup.

Tip: Inspect both the HTML and the Network tab in DevTools. You might discover XHR (XMLHttpRequest) calls that return the data you need in a clean format, eliminating the need for complex parsing.

Parallelize Requests for Faster Scraping

As your scraping project grows, making requests one at a time won’t be fast enough. Parallelizing requests is a great way to speed things up by making multiple requests at once. However, you need to be careful not to overload the server. One way to do this is by using a queue to keep track of the URLs that need to be scraped. You can then control how many requests run at the same time, making sure you don’t send too many all at once. This way, you can scrape faster without causing any issues.

import asyncio
import aiohttp
async def fetch_url(session, url):
async with session.get(url) as response:
return await response.text()
async def fetch_all(urls):
async with aiohttp.ClientSession() as session:
tasks = [fetch_url(session, url) for url in urls]
return await asyncio.gather(*tasks)
urls = ["http://example.com", "http://example2.com"] # Add more URLs
htmls = asyncio.run(fetch_all(urls))
for html in htmls:
print(html)

By using asynchronous requests, you can handle hundreds or thousands of URLs in parallel, vastly improving the efficiency of your scraper.

Comply with Robots.txt and Legal Considerations

Always check a website’s robots.txt file before scraping. This file contains guidelines set by the website owner on what can and cannot be scraped. While not legally binding, it’s a good practice to follow the rules specified in robots.txt.

• Crawl-delay: If specified, respect the crawl delay to avoid overloading the server.
• Disallowed pages: Avoid scraping pages that are disallowed in the robots.txt file.

Additionally, it’s important to consider the legal implications of web scraping. In some jurisdictions, scraping without permission may violate terms of service agreements, intellectual property rights, or data privacy laws. Always ensure you’re scraping ethically and within the bounds of the law.

Critical Don’ts to Keep in Mind

While there are numerous best practices to follow, there are also common mistakes that can hinder your web scraping project or even lead to your scraper being blocked entirely. Let’s explore the key don’ts.

Don’t Use Headless Browsers for Everything

Headless browsers like Selenium, Playwright, and Puppeteer are incredibly powerful tools for scraping websites that rely on JavaScript. However, using these tools for all your scraping tasks is inefficient and resource-intensive. Headless browsers consume more CPU and memory, and they can be much slower than simpler methods like using requests or cURL.

Before resorting to headless browsers, always check if the content you need is available directly in the HTML. If so, use traditional scraping methods to avoid unnecessary overhead.

Don’t Couple Your Scraper to One Target Website

As your scraper grows, it’s essential to separate the scraping logic that is specific to a particular website from the general scraping framework. This separation makes it easier to maintain and expand your scraper.

For example, the code responsible for fetching the HTML, parsing it, and storing the data should be independent of the website’s specific structure. This will allow you to adapt the scraper to different websites with minimal changes.

Don’t Overwhelm Small Websites with Requests

While it’s tempting to scrape hundreds of pages from large websites like Amazon or eBay, remember that small websites with limited resources can easily be overwhelmed by heavy scraping. Excessive requests can cause the site to crash or significantly degrade its performance, leading to a negative experience for other users.

To prevent this, always throttle your requests, respect the Crawl-delay directive, and be mindful of the website’s scale. If you’re scraping a small site, consider reducing the frequency of your requests or scraping at off-peak hours.

Don’t Mix Headers Across Browsers

Websites often use sophisticated anti-bot measures that involve checking the headers sent by the client. If you mix headers from different browsers or use an invalid header set, you might raise red flags. For example, a real iPhone browser doesn’t send the same set of headers as Chrome or Firefox.

When rotating User-Agent strings, ensure the entire header set is valid for the browser version you’re simulating. This includes headers like Sec-Ch-Ua and Accept-Language. Using incomplete or mismatched headers can lead to your scraper being flagged as a bot.

Don’t Ignore Error Handling

Even with all the best practices in place, things can still go wrong. Websites may change their structure, proxies might fail, or your scraper might encounter unexpected errors. Always implement robust error handling to gracefully manage issues like timeouts, connection errors, or parsing failures.

try:
response = requests.get("http://example.com")
response.raise_for_status() # Raises an error for bad responses
except requests.exceptions.RequestException as e:
print(f"Error occurred: {e}")

Conclusion

Web scraping can be an incredibly powerful tool when used effectively. By following the best practices outlined above, you can build scrapers that are efficient, ethical, and scalable. However, it’s essential to balance the need for speed with respect for the target website’s infrastructure and legal guidelines.

Remember that web scraping is not a one-size-fits-all approach. Depending on the website, you may need to adjust your scraper’s behavior, from rotating IPs and headers to determining whether a headless browser is necessary. Ultimately, By being careful and planning your web scraping, you can avoid common mistakes and collect the data you need without getting blocked or causing problems for the site.