9 Web Scraping Challenges To Be Aware Of

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Web scraping is becoming more popular than ever. Real-time data is valuable, and companies need it for various use cases. Businesses scrape data to analyze customer preferences, set business goals, track market trends, and use it for many other important tasks. 


However, data collection comes with challenges. Being aware of them is the first step toward avoiding the most common issues. Here you’ll find nine common web scraping challenges and suggestions for solving them.

Table of Contents
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1. Robots.txt

Web scrapers need to check robots.txt file before gathering data

Bot access is the first thing to check before scraping a website. To learn whether the target site allows you to collect data, check the website’s robots.txt file. Sites can block automatic data collection for numerous reasons, and you can never assume that a website will allow scraping. 


If your target website forbids bot access, the solution would be to contact the web owner and ask for permission. Listing the reasons why you wish to scrape their site may increase your chances of getting access. However, if they don’t grant the bot access, you’ll need to search for an alternative site to scrape.


Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is used to distinguish human users from bots by providing them with a puzzle to solve. These may be a set of various images that only differ slightly. Most bots are unable to tell the difference, while humans would solve them without a glitch. 


This challenge can be solved by integrating a CAPTCHA solver into your bots. These services commonly use sophisticated artificial intelligence and machine learning algorithms.


However, the best solution is to avoid CAPTCHAs altogether, as they slow down the web scraping process. The key to avoiding CAPTCHAs is acting as a regular human user on your target websites. This can mean rotating proxies, slowing down scraping rates, setting up the correct user agent, using a headless browser, etc.

3. Changing Web Structure

While most websites are built on HTML (Hypertext Markup Language), different web designers may create their sites based on various structures. This means that each website may require a separate web scraper that would work on a specific website structure.


However, website content can be updated by adding new features and making structural changes. This means that a web scraper built for a certain page may stop working after the page is updated. 


The solution to this challenge is constantly monitoring and updating your scrapers to match the web structures.

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4. Dynamic Web Content

Many websites use Asynchronous JavaScript and XML (AJAX) to update dynamic web content. AJAX creates asynchronous web applications that may improve the user interface on the website. For example, it may include lazy-loading images, infinite scrolling, and AJAX calls via certain buttons. While this may improve the UX on web pages, it’s a challenge for data extraction. 


If you need to extract data from AJAX-driven websites, the best solution is to use additional software or a headless browser. You can also find free, open-source projects with extensive documentation that will help you solve this challenge.

5. IP Blocking

Blocking IP addresses is one of the most common data scraping challenges. The good news is that this challenge can be bypassed. 


Websites may try to stop scrapers by blocking an IP address. This happens if the scraper sends too many access requests or makes parallel requests to the same target. The website then identifies the IP address as suspicious and blocks it completely or restricts its access to the site.


The best way to avoid IP blocking is to send requests at certain intervals and limit the number of requests. You can also rotate your IP addresses so that not all requests come from the same IP.

6. Honeypot Traps

Honeypot traps are an anti-scraping mechanism

Honeypot traps are a technique used by website owners to stop web scraping on their pages. These traps are usually links located in the HTML code elements, which are invisible to regular website users. Web scrapers click on the links and may get redirected into an infinitive loop of nowhere-leading requests. This can also help the website to spot a scraper and block it.


Reliable proxies can help avoid honeypot traps since free proxies from a questionable source may be more prone to falling into the trap. Another solution is to set your software to look for certain CSS elements, such as “visibility: hidden” and “display: none”, as these may indicate the honeypot traps.

7. Loading Speed

Slow or unstable website loading speed may break up the scraping process. This happens when the website receives too many requests and cannot handle them. 

When humans run into a slow-loading website, they can easily solve it by reloading the site. However, this is a problem for web scrapers. 


To solve this issue, the scraper may be preset to automatically retry sending requests to the target website.

8. Data Under a Login

Data under a login can be challenging to scrape

In some cases, you may need to access data that’s kept under a login. When a regular user needs to log in, they submit their login credentials. The browser then automatically adds a cookie value to a number of requests running on other sites. This way, it knows you’re the same user who logged in before. 


When scraping data under a login, you need to send cookies together with your requests, and this will solve the issue.

9. Scraping in Real-Time

Real-time data scraping is required for many business use cases. For example, fresh data is essential for comparing prices, competitor monitoring, and inventory tracking. This may become a challenge when scraping at a large scale. There’s often a delay between requests and data delivery, which can cost a lot of money for companies. 


To ensure your scraper runs smoothly and delivers data in real-time, you need to use reliable and fast proxies. Your scraper must also be constantly maintained to reduce the risk of breaking at the worst time.

Block-Free Web Scraping with Datacenter Proxies

Choosing reliable proxy services can be a defining factor in the success of web scraping operations. It’s important to pick a provider that offers proxies with high uptime. 


When it comes to the right proxy type, it may depend on your web scraping needs. However, datacenter proxies are one of the most effective proxies for data gathering because they’re fast and cheaper than other types of IPs. These proxies can be used with any web scraping tool and can be rotated to avoid various data collection challenges such as IP blocks.


If you’re not sure about certain proxies, some providers offer free proxies that can be used for testing IPs before you commit to buying them. While free proxies found on the internet without a clear source can be harmful and even stop the web scraping jobs, free IPs from reliable proxy servers can be very beneficial.


Companies and individuals constantly collect data for various use cases. However, extracting data can be challenging since websites contain various anti-scraping mechanisms. The main web scraping challenges include bot access blocking, CAPTCHAs, changeable web structure, dynamic content, IP blocks, honeypot traps, loading speed, login systems, and real-time data delivery. 


Solving these challenges requires certain knowledge and skills as well as constant monitoring and upkeep of your web scraper. Using a headless browser, setting up the right user agent, and similar actions can also help avoid the challenges. Using reliable proxies can also be an effective solution to most of the mentioned issues. 


Datacenter proxies are an excellent option for extracting data at a large scale. These IPs are fast and cheaper, and choosing a reliable proxy service provider will ensure that these proxies are also reliable.

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