Best Practices for Web Scraping in 2025

If you’ve been trying to scrape websites but keep running into blocks, missing data, or slow performance, you’re not alone. Web scraping best practices can make all the difference between frustration and success. Scraping isn’t just about writing a script to pull data—it’s about doing it the right way so your scraper runs smoothly and consistently.

Maybe your requests are getting blocked, or the data you’re collecting isn’t complete. You might be scraping too fast, not rotating IP addresses, or missing essential request headers. Or maybe the website has anti-bot measures that your scraper isn’t built to handle. The good news? These problems can be fixed.

In this article, you will learn the best ways to improve your scraping process, avoid common mistakes, and get the results you’re looking for. 

Ready? Let’s dive in!

What Is Web Scraping?

At its core, web scraping is the process of automatically extracting data from web pages. Instead of manually copying and pasting information, a web scraper—a program or bot—sends requests to a website, pulls the data you need, and organizes it into a structured format, like a spreadsheet or database.

If you’ve tried web scraping before, you know that getting the data isn’t always as simple as it sounds. Some websites load content dynamically with JavaScript, making it harder to scrape. Others have security measures in place to detect and block bots. That’s why having the right strategy is key.

Why Web Scraping Matters

Web scraping lets you gather large amounts of data quickly, making it easier to stay ahead in your industry. Here are some ways it can help you:

• Market research: Want to keep an eye on your competitors? Web scraping helps you track their prices, product trends, and customer reviews so you can make smarter business decisions.
• E-commerce monitoring: Scraping can help you track price changes, stock availability, and new product launches if you sell online. This way, you can adjust your pricing, manage inventory, and stay competitive.
• Social media analysis: Scraping social media gives insights into trending topics, audience sentiment, and engagement levels. Whether you’re running a business or researching trends, this data helps you understand what people are talking about.
• SEO and search engine tracking: Want to improve your search rankings? Scraping lets you monitor keyword performance, analyze competitor strategies, and keep up with content trends to optimize your SEO efforts.
• Data collection for AI and machine learning: If you’re working with AI, you need good datasets. Web scraping helps you gather training data for everything from language models to image recognition, saving time and improving accuracy.

When done right, web scraping saves you hours of manual work and gives you access to valuable insights. But to get the best results, you need more than just a script—you need the right scraping techniques.

Web Scraping Best Practices for Efficient Data Extraction

If your scraper keeps getting blocked, returning incomplete data, or running slower than expected, there’s a good chance you’re missing some key best practices. Web scraping isn’t just about writing a script—it’s about ensuring your requests are optimized, efficient, and respectful of the website you’re scraping.

Below, we’ll go over the most essential best practices to help you scrape successfully while avoiding common mistakes.

1. Use IP Rotation to Avoid Blocks

If you send too many requests from the same IP address, websites may flag your activity as suspicious and block you. IP rotation helps by switching between IPs, making your requests look more natural.

How to do it:

• Use proxy services like ScraperAPI that provide rotating IPs.
• Switch between residential, data center, or mobile proxies depending on your needs.
• Rotate IPs frequently, especially for high-volume scraping.

This keeps your scraper running smoothly and lowers the chances of getting blocked. Take a look at our guide for a complete list of the best proxies.

2. Set Request Delays to Mimic Human Behavior

If your scraper sends requests too quickly, websites might detect it as a bot and block you. Real users don’t load dozens of pages per second, so adding minor delays between requests can help you stay under the radar.

How to do it:

• Use randomized request delays instead of fixed delays to make your traffic look more natural.
• Avoid scraping during a website’s peak hours when traffic is high, as this can slow down the site and increase the chances of detection.
• Implement exponential backoff, where your scraper increases the wait time if a request fails, reducing the risk of getting banned.

A well-paced scraping process keeps your requests steady without overwhelming the website.

3. Rotate User-Agent Headers and Other Request Headers

Most websites check the User-Agent header to identify what kind of browser or bot is making a request. If you use the same User-Agent string, your scraper becomes easily detectable.

How to do it:

• Rotate between multiple User-Agent strings from different browsers and devices.
• Add other headers like Referer, Accept-Language, and Accept-Encoding to make your requests look more legitimate.
• Avoid using outdated or generic user agents like "Python-urllib/3.9"—these are often flagged as bot traffic.

A properly configured request header makes your scraper blend with standard web traffic.

4. Use Headless Browsers for JavaScript-Rendered Content

Some websites load their content dynamically using JavaScript, meaning a simple request to the URL won’t return the data you need. In these cases, you’ll need a headless browser like Selenium, Puppeteer, or Playwright to render the page before scraping.

How to do it:

• Use Puppeteer or Playwright to scrape modern, JavaScript-heavy websites.
• If you prefer Python, Selenium is a great option, though it tends to be slower.
• Optimize browser-based scraping by caching responses or disabling unnecessary CSS, images, and fonts to improve speed.

Headless browsers are powerful but resource-intensive, so only use them when necessary.

5. Respect the Website’s robots.txt File

The robots.txt file tells crawlers which pages they can or cannot scrape. While it’s not legally enforceable, respecting these rules helps maintain good scraping etiquette and avoid unnecessary blocks.

How to do it:

• Always check the robots.txt file before scraping a site. You can usually find it at example.com/robots.txt.
• Look for Disallow directives, which indicate pages the site owner prefers scrapers to avoid.
• To automate this step, use Python’s Scrapy or BeautifulSoup with built-in robots.txt parsing.

Ignoring robots.txt may not always get you banned, but it increases the risk of detection and scraping disruptions.

6. Use Session Persistence to Avoid Suspicion

Most scrapers send a fresh request with a new session each time, but real users often browse multiple pages in the same session. Some websites track session activity and may flag scrapers that don’t maintain a session over time.

How to do it:

• Use cookies and session tokens to maintain continuity between requests.
• Store and reuse session-related headers, such as Authorization tokens and CSRF tokens, when scraping authenticated pages.
• Rotate session identifiers less frequently than IPs to make requests appear more human-like.

By maintaining sessions properly, you make your scraper behave more like a real user, reducing the risk of detection.

7. Detect and Bypass Honeypot Traps

Some websites use honeypot traps—invisible links or hidden form fields that real users never interact with, but bots often do. Clicking or submitting these elements can instantly flag your scraper.

How to avoid them:

• Before scraping, analyze the HTML and CSS for hidden elements (e.g., display: none;, opacity: 0;, or position: absolute; left: -9999px;).
• Avoid automatically clicking on every link or submitting every form field.
• Use browser automation tools like Selenium or Puppeteer to mimic user behavior and skip suspicious elements.

Recognizing honeypot traps before scraping helps avoid unnecessary bans and keeps your scraper running longer.

8. Randomize Click Patterns and Mouse Movements in Headless Browsers

When using headless browsers, one common mistake is sending clicks and interactions in a perfectly structured way. Real users don’t click buttons or scroll pages in exact intervals, and some sites use behavior tracking to spot scrapers.

How to do it:

• Use randomized mouse movements instead of instant jumps when interacting with elements.
• Add slight variations in scrolling speed, click locations, and form inputs to mimic human behavior.
• If you’re using Selenium or Puppeteer, generate realistic delays between interactions instead of running everything instantly.

This extra layer of human-like interaction can help you bypass more advanced bot detection systems.

9. Cache Responses to Reduce Unnecessary Requests

If you’re scraping the same website frequently, you might send duplicate requests without realizing it. This slows down your scraper and increases the chance of detection.

How to do it:

• Store previously scraped data in a local cache or database and only make new requests when necessary.
• Use ETags and Last-Modified headers to check if the content has changed before re-downloading a page.
• Implement caching strategies like memory caching or disk caching to store and reuse responses efficiently.

Caching helps reduce server load and makes your scraping process faster while minimizing unnecessary interactions with the target website.

10. Use Distributed Scraping for Large-Scale Projects

If you’re scraping a large-scale website, running everything from a single machine can slow down performance and make it easier to get blocked. Distributed scraping spreads requests across multiple systems to improve speed and reliability.

How to do it:

• Use cloud-based solutions like AWS Lambda, Google Cloud Functions, or ScraperAPI to distribute requests.
• Deploy scrapers on multiple virtual machines or servers to balance the load.

Distributing your scraping tasks across multiple machines speeds up data collection and makes it harder for websites to detect and block your scrapers.

11. Scrape Ethically to Maintain Long-Term Access

Even if a website doesn’t explicitly block scraping, scraping responsibly is essential to avoid causing harm or getting permanently blocked. Ethical scraping ensures you can continue gathering data without disrupting the website’s functionality.

How to do it:

• Respect rate limits – Don’t overload a server with excessive requests in a short period. Stick to reasonable request intervals.
• Check the website’s terms – Some sites have specific rules about data extraction. Reviewing their robots.txt file and terms of service can help you stay compliant.
• Avoid scraping sensitive or personal data – If a site contains user-generated content or private information, ensure your scraping activities don’t violate privacy expectations.
• Provide value where possible – If you’re scraping frequently from a particular site, consider attributing the data source or contacting the site owner to discuss potential partnerships.

For a deeper dive into ethical scraping, check out this guide on ethical web scraping.

Best Tools for Web Scraping and When to Use Them

Choosing the right web scraping tool can make a huge difference in efficiency, accuracy, and ease of use. Whether you need a simple library to parse HTML or a full-featured API to handle complex scraping challenges, there’s a tool for the job.

1. ScraperAPI – Best for Avoiding Blocks and Scaling Up

ScraperAPI is a web scraping API that manages the most challenging parts of web scraping for you, including IP rotation, CAPTCHA solving, and headless browsing. Instead of dealing with proxy management or anti-bot protections manually, you simply send a request through ScraperAPI, which returns the scraped data without interruptions. This makes it an excellent solution for large-scale scraping projects where avoiding blocks is critical.

2. Scrapy – Best for Large-Scale Web Crawling

Scrapy is a fast and powerful Python framework designed for large-scale web crawling and structured data extraction. It supports asynchronous requests, built-in handling of the robots.txt file, and efficient data storage options.

3. BeautifulSoup – Best for Simple HTML Parsing

BeautifulSoup is a lightweight Python library that simplifies HTML and XML parsing. It’s ideal for extracting specific elements from web pages without complex crawling logic.

4. Selenium – Best for Scraping JavaScript-Heavy Websites

Selenium automates browser interactions, making it helpful in scraping pages that rely on JavaScript to load content. It supports multiple browsers and allows actions like clicking buttons, filling forms, and handling pop-ups.

5. Puppeteer – Best for Headless Browser Automation

Puppeteer is a Node.js library that provides programmatic control over Chrome or Chromium in headless mode. It’s useful for rendering dynamic content, taking screenshots, and interacting with elements that require JavaScript execution.

No single tool is perfect for every web scraping project. Combining multiple tools—such as using Scrapy for large-scale crawling, Selenium for handling JavaScript, and ScraperAPI to avoid blocks—can create a complete solution. Choosing the right combination depends on the complexity of the website, the volume of data you need, and the challenges you want to overcome.

Main Challenges When Scraping Websites at Scale

Let’s explore the 5 most common challenges you’ll face when scraping the web at a large scale:

1. Client-side Rendering

So you’ve visually inspected the website you want to scrape, identified the elements you’ll need, and run your script. The problem is that scrapers can only extract data from what they can find in the HTML file, and not dynamically injected content.

This is the most common roadblock you’ll find when scraping JavaScript-heavy websites. Because AJAX calls or JavaScript are executed at runtime, it makes it impossible for regular scrapers to extract the necessary data.

2. Anti-scraping Techniques

There are several ways websites protect their data from scraping scripts. These techniques analyze a number of metrics and patterns to make sure it is a human who is browsing the site and not a robot.

A simple example of this is analyzing the number of requests from the client. If a client makes too many requests within a particular time frame or there are too many parallel requests from the same IP, the server can go ahead and blacklist the client.

Servers can also measure the number of repetitions and find request patterns (X number of requests at every Y seconds). By defining a threshold, the server can automatically blacklist any client exceeding it.

3. Honeypots

Honeypots are link traps webmasters can add to the HTML file that are hidden from humans but can be accessed by web crawlers. This is as simple as adding a CSS property of display:none to the link or blending it to the background. When a web scraper accesses the link, the server can determine it is a robot and not a human, and blacklist the client over time.

4. CAPTCHAs

By redirecting the request to a page with a CAPTCHA, the server creates a challenge our web scraper needs to solve to prove it’s human. This is an effective security mechanism and prevents automated programs from accessing the page.

5. Browser Behaviour Profiling 

Because servers can measure how a client interacts with the site, anti-bot mechanisms can spot patterns in the number of clicks, clicks’ location, the interval between clicks, and other metrics and use this information to blacklist a client.

Most of these challenges are easy to work around using ScraperAPI as long as you’re setting the scraper correctly.

To help you build a scraper more efficiently and avoid bans, you need to make sure you’re implementing a set of best practices.

How to Prevent Getting Blocked While Web Scraping with ScraperAPI

ScraperAPI takes care of IP rotation, CAPTCHA solving, and headless browsing, but using it the right way can make your scraping even more efficient. 

Here are a few key settings and features to help you get the best results:

• Set Your Timeout to at Least 60 Seconds: ScraperAPI keeps retrying failed requests with different proxies and headers for up to 60 seconds. If your timeout is too short, your connection might cut off before the API has a chance to succeed, leading to unnecessary failed responses.
• Let ScraperAPI Handle Headers Unless You Need Custom Ones: ScraperAPI automatically picks the best User-Agent, cookies, and other headers for each request. Overriding them without a good reason can actually make your scraper more detectable.
• Always Use HTTPS to Avoid Redirect Issues: If a website defaults to HTTPS, sending requests to the HTTP version can trigger a redirect. This adds extra load time and increases the chances of your request being flagged as a bot.
• Only Use Sessions When Necessary: ScraperAPI supports session-based scraping, but the session proxy pool is smaller than the main pool. Overusing sessions can lead to higher failure rates, so only use them if your scraper needs to maintain state between requests.
• Manage Concurrency to Stay Within Limits: ScraperAPI has a limit on concurrent requests depending on your plan. If you’re scraping at scale, setting up a central cache (like Redis) can help distribute requests more efficiently and avoid hitting concurrency limits too quickly.
• Enable JavaScript Rendering Only When Needed: Turning on JavaScript rendering (render=true) lets ScraperAPI load JavaScript-heavy pages, but it also takes longer and reduces the number of retries per request. Use it only for sites that require JavaScript to display important data.
• Enable Geotargeting for Location-Specific Data: Some sites serve different content based on location. If you need country-specific data, use ScraperAPI’s geo-targeting feature by adding the country= parameter to your requests.
• Use Structured Data Endpoints to Save Time: Instead of dealing with raw HTML and manual parsing, ScraperAPI’s Structured Data Endpoints (SDEs) return clean, structured JSON data from sites like Amazon, Google, Walmart, and eBay. This speeds up your workflow and eliminates the need for extra processing.
• Automate Large-Scale Scraping with DataPipeline: For high-volume scraping, ScraperAPI’s DataPipeline Endpoints let you schedule and manage jobs programmatically. You can process bulk requests asynchronously, receive data directly via webhooks, and let ScraperAPI handle timeouts, retries, and bans automatically.

Tuning these settings to match your scraping needs will help you get the best performance from ScraperAPI while reducing failed requests and making your workflow more efficient.

Conclusion

Web scraping can be powerful, but it’s easy to run into blocks, slow performance, or unreliable data without the right approach. By following web scraping best practices, you can scrape efficiently while respecting website limits and avoiding common pitfalls.

Using the right tools makes all the difference. Whether you need a lightweight HTML parser, a headless browser, or a full scraping API, choosing the best setup for your project will save you time and effort. ScraperAPI simplifies the process by handling IP rotation, CAPTCHA solving, and JavaScript rendering, allowing you to focus on extracting the data you need without interruption.

If your scraping results haven’t been as smooth as you’d like, implementing the strategies in this guide will help improve performance and reliability. Take the time to fine-tune your scraper, test different configurations, and adjust your settings based on the website you’re working with. The better optimized your scraping setup is, the more reliable your data collection will be.

Now that you have a solid understanding of web scraping best practices, you’re ready to start scraping smarter and more efficiently!