Top 5 Challenges in Web Scraping: Solutions and Best Practices

Technological advancements and the growing importance of data have forever changed how businesses use the internet. Information gathered externally about customers and competitors allows organizations to gain an edge and find new opportunities for success. Large-scale web scraping gives enterprises the resources to perform the data analysis that drives critical business decisions.

But many organizations encounter problems when attempting to operate multiple web scrapers. Let’s look at some of the biggest challenges a business might run into and the web scraping solutions to overcome them.


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What Is Meant by Web Scraping?

What Is Meant by Web Scraping?

Web scraping refers to the automated process of extracting data from websites. Companies can run robots to collect everything from customer feedback on social media sites to product price changes on a competitor’s website.

Automated web scrapers typically load different URLs of target sites from which they wish to collect information. From there, they load a page’s HTML code. Some advanced web scraping solutions can render an entire website. The scraper extracts and outputs the desired information in the designated format. Web scraping solutions typically copy data to a comma-separated values (CSV) file before uploading it to a database.

Web scraping is the preferred method to collect a lot of information. Let’s say a company wants to collect product information from the websites of thirty different competitors. Using a web scraping bot to download and store the data is much faster than assigning the task to a human worker.

Companies can build their own web scraper or purchase web scraping solutions from a third party. The latter is ready to download and start running. However, some users may need a customized scraper, depending on their needs.

While businesses gain a lot from web scraping solutions, they present some hurdles. Many websites make web scraping difficult by deploying blocking mechanisms. Structural page issues can also make information collection difficult. Here are some of the most significant issues and how to overcome them.

1. Anti-scraping Technology

1. Anti-scraping Technology

Businesses often employ anti-scraping measures to protect their websites against scraping. If web scraping solutions aren’t used responsibly, they can slow down or even crash a website. Companies also worry about the potential theft of confidential information and customer data. Let’s look at some standard anti-scraping tools and how to get around them.

IP tracking

Many websites use IP tracking to determine whether a visitor is a human or a robot. The website looks for behaviors inherent to automation, including:

  • A process that visits a page multiple times in seconds: It’s unlikely that a human could browse that quickly.
  • A process that conducts website visits at the same pace each time: Human visitors don’t repeatedly exhibit the same behavior patterns. If a website sees your web scraping solution behaving that way, it will trigger an IP ban.
  • A process that sends the same number of requests or visits a website at the same time every day: Human behavior is rarely so consistent.

Any of the above behaviors can get your web scraping solution blocked. Below are some ways to keep your automation’s IP address off website ban lists.

  1. Slow down the pace of your web scraping solutions. Try setting up a sleep or delay function before execution. You can also increase the wait times between delays before attempting more data extraction.
  2. Enact a random for each step of your web scraping solution. That will make its movements seem more human-like to anti-scraping technology.
  3. Use a web proxy to periodically change the IP addresses for your web scraping solutions. Requests sent through a rotated IP proxy service make the web scraping tool seem less like a robot, lowering the risk of getting blocked.


Chances are you’ve seen a Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) popup when browsing a website. They present you with a checkbox to confirm that you are not a robot. The site may ask you to select specific pictures in a grid or enter character strings into a box. These public automatic programs help determine if a web visitor is a human or a robot.

In the past, bots had to go through a lot to bypass CAPTCHAs. Now many open-source tools are capable of solving them. Businesses with programmers on staff often build proprietary libraries and image recognition techniques using machine learning (ML) to get past CAPTCHA checks.

However, the easiest way is to avoid it in the first place. You can slow down or randomize the extraction process. Rotated IP addresses and time delays between data extraction can help your web scraping solutions avoid detection.

Login pages

Many social media platforms require users to log in, so you need web scraping solutions capable of circumventing this process. Getting around a login page requires a bot capable of simulating login keyboard and mouse operations like clicking the “log in” button or typing an account name and password. Once your chosen web scraping solution is logged in, it would need to save a cookie (a piece of data that stores user browsing data), allowing the crawler to start scraping information.

Because some websites limit data extraction during a specific period, you may need to set your web scraping solution to only run once daily or at another limited frequency. That way, it can collect and send the latest information back to the organization.


User-Agent, or UA, is a header used by websites to identify how a user visits its landing pages. A UA captures data from web scraping solutions like browser language, the operating system used, and the CPU type. The information is combined with more parameters to create a digital fingerprint unique to each visitor.

The UA follows a web scraping process wherever it goes. It’s one of the primary methods websites use to identify a robot. Web crawlers without a header would show up as a script, which would cause the website to block their requests.

Your web scraping solution would need to appear as a browser with UA headers. Because websites sometimes show different pages and information based on browser type, you would need multiple browsers and versions to fit each data request.

Another way to get past digital fingerprinting and user agents is by using a headless browser. A headless browser operates without using a graphical user interface. They execute from a command line or through network communication. You can also use a library to manually set up a digital fingerprint.


Asynchronous JavaScript and XML (AJAX) is a technique used to perform asynchronous website updates. Synchronous updates involve reloading the entire web page after any change, but asynchronous updates only reload the places where the changes occurred.

Web crawlers would need to detect the changes that occur in the URL when the page changes. From there, the web scraping solution could generate batches of URLs, then extract information directly. This avoids the need to recode a web scraping robot to navigate the page in a human-like manner.

2. Large Data Sets

2. Large Data Sets

Large-scale web scraping requires the support of a robust infrastructure. That means a system capable of supporting multiple web scraping solutions that crawl and pull content from many websites simultaneously.

For example, if a competitor has a website with over 100,000 pages with about 10 articles each, your robots would need to perform 100,000 requests. If it takes each website two seconds to load, that equates to more than two days of sending requests and waiting for the page to load.

That doesn’t even cover the time it takes to extract the data. As you can see, it would be nearly impossible for a company to manually get the information they need. With a large-scale web scraping infrastructure, businesses can generate requests from a server. That cuts the time it takes to send and load requests to a few hundred milliseconds.

Another benefit of a large-scale scraping system is that you can run web scraping solutions in parallel, extracting information from multiple pages per second. One process could collect user reviews from Facebook, while another could focus on capturing new products added to a competitor’s catalog. However, that can lead to performance issues that further slow down the website.

Businesses may be better off setting up multiple small web scraping solutions versus relying on one large one. You can task each scraper with getting specific information from a website, allowing you to pull data from multiple processes simultaneously.

A proxy server can help you avoid having too many requests from multiple web scraping solutions using the same IP. It acts as an intermediary between your data collection robot and the target website.

It’s also a good idea to constantly update your scrapers. Implementing a logging system helps you track whether everything works as intended. It also makes it easier to spot and address issues.

3. Structural Web Page Changes

3. Structural Web Page Changes

Companies sometimes revamp their websites to improve the layout, design, and available features. While that makes for a better user experience, these updates can complicate things for web scraping solutions. They’re typically constructed to work with a specific page layout.

Changes to the page impact the parameters, which means you need code adjustments. Otherwise, your scraper could return incomplete data sets or crash. Other challenges that can affect the operation of a web scraping solution include:

  • Inconsistent markup
  • Nested structures
  • Irregular data formats

It’s more difficult for web scraping tools to accurately collect data from these formats. Inconsistent markup makes it harder to find information. Some websites may have dates formatted differently, meaning more work is needed to make them legible. Nested structures, like drop-downs, can be difficult to navigate correctly to find data.

You can manage these changes using parsers capable of adjusting on the fly when page updates happen. You can also use AI-enabled web scraping solutions to navigate adjustments to the layout, like new prices or product descriptions.

It also helps to set up automated website monitoring to track HTML structural changes. You can also have your web scraping solution look at CSS selectors to locate page elements. Targeting attributes, classes, and tags makes web scrapers more capable of handling website updates. You can also program web scrapers to retry extraction attempts after an update once a page stabilizes.

4. Real-Time Latency

4. Real-Time Latency

Performance bottlenecks can crop up when conducting large-scale web scraping. You might have tasks designed to send HTTP requests and then send captured information to a database. There can also be CPU tasks like parsing HTML code, loading JSON information, and performing natural language parsing.

All of the above can lead to latency issues with web scraping solutions. Code optimization is the first step you should take if you’re having problems with slow data collection. That doesn’t mean developers have to go back to the drawing board. Web scrapers built with languages like Python typically have libraries available to help with large-scale scraping tasks with minimal overhead.

Other ways to alleviate data scraping latency include:

  • Make smart collection decisions: Be selective about the information collected. Try to make sure you’re only targeting and scraping essential data.
  • Keep performance in mind: Track the overall performance of your web scraping solutions. Use asynchronous programming to handle multiple requests concurrently, allowing for faster data retrieval. Review robot logs and metrics to figure out how you can optimize their efficiency.
  • Use more efficient scheduling: Try to execute your web crawlers during off-peak hours. Operating when web traffic is lower gives your processes more resources and can improve response times.
  • Try to reduce the server load: Avoid overwhelming a web server with multiple rapid requests. Try adding delays between each submission to slow things down. Using rotating proxies can reduce the load on a target site and speed up data scraping solutions.
  • Avoid unnecessary requests: You can try saving data locally to avoid making repeated page requests. That speeds things up when you need to access previously scraped information. Headless browsers help with rendering dynamic content, making data retrieval more efficient.

5. Dynamic Content

5. Dynamic Content

Dynamic websites store their content using the server or client in addition to HTML. The information generated can vary based on a user’s actions. Clicking one button can generate an entirely different page than scrolling. That makes website pages load more quickly because it’s not reloading the same information for each request.

You can identify if a webpage uses dynamic content by disabling JavaScript in your browser. That will cause the content to disappear.

You can still work with dynamic content using a web scraping solution built with Python or another coding language. Keep in mind that all dynamics pages operate differently. Some may use API, while others store JavaScript-rendered content as JSON within the Document Object Model (DOM). Below are some solutions you can try when navigating dynamic web content.

  • Headless browser: Headless browsers execute JavaScript and load and render dynamic page content.
  • Wait mechanism: Because dynamic content can take some time to load, try adding a wait to your web scraping solution. It should only start collecting data once specific elements appear or certain events are triggered.
  • Network activity: Content loaded through AJAX requests or APIs can be tracked through network activity. Track what’s happening in the network using developer browser tools, then mimic the requests in your web scraping code.
  • Pagination and scrolling: Update your web scraper to move through multiple pages or to keep scrolling for additional content.

Web Scraping Ethical Issues and Best Practices

Web Scraping Ethical Issues and Best Practices

Data scraping has become ubiquitous in many industries, including marketing and journalism. Because there are so many processes collecting information for various purposes in use for different purposes, organizations may have questions about what’s ethical and what’s not when it comes to running data scraping solutions. Let’s examine some of the biggest concerns.

1. Avoid breaking websites

Web scrapers send repeated requests to websites and pull information from many pages. Servers must process the requests for each page, then send a response back to the client. That consumes a lot of resources, meaning the servers cannot promptly accommodate requests from other users.

Too many requests overrun finite resources and can crash the website. It’s a technique often deployed by hackers called a Denial of Service (DoS) attack. Because they’re such a common occurrence, businesses invest a lot of money to keep outside processes from using too many server resources.

They look for any unusual number of site requests from a single IP address. The easiest way for companies to protect their site is by blocking additional requests from that computer. Web scrapers used for legitimate purposes often get caught up in that net. Utilizing a rotating IP proxy or retry mechanism can help your process avoid being mistaken for a DoS attack.

2. Respect intellectual property

Keep in mind that other countries have different standards when it comes to data collection. Practices that might be legal in the U.S. may be illegal in another territory. You should also look at the terms of service for any website accessed anywhere in the world. Violating those conditions could result in you being subject to legal penalties and fines.

Publicly available information, meaning data not protected by a password or other authentication verification, is generally OK to scrape. Again, you want to ensure you don’t send so many requests that you bring the website down. What’s never OK is downloading content from one site to display without permission on another. You shouldn’t copy an article written by someone and present it as your own, for example.

Many countries have copyright laws recognizing “fair use” when reusing specific copyrighted material. The exceptions are narrow, so it’s essential that companies not make assumptions about whether it applies or not. If you have questions, it’s always good to have your legal team review whether you are within your rights to collect and use certain information.

3. Stick to public data

This goes together with respecting copyrights. Business collection processes should only access publicly available data unless you have specific permission from the website owner. For example, your business may find setting up a subscription to certain research websites beneficial. Make sure you stick to the guidelines of your agreement around using specific information.

You shouldn’t be doing anything that requires attempts to get around logins or other access restrictions. You can often avoid issues by using a publicly available API. It may be able to provide information not available through your data scraping solutions.

4. Collect only what you need

Only scrape data you absolutely need. If you have a process collecting information you can’t tie back to a specific business purpose, shut it down. Sometimes you only need data for one project. Once it’s over, make sure you don’t have random web scraping solutions running and unnecessarily consuming website resources. That can cause your IP addresses to end up on blocklists, rendering them unusable.

Try to start small with a new web scraper. You can always scale the solution up to collect more data if necessary. The goal is to avoid getting blocked. Limit your testing to sites that freely allow for data collection. That will enable you to practice other steps, like organizing information for further data analysis.


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Overcome Web Scraping Challenges With Rayobyte

Overcome Web Scraping Challenges With Rayobyte

Web scraping allows organizations to collect data to support their business processes. However, there are some roadblocks web scraping solutions can encounter. Web scraping solutions will need to navigate getting around anti-scraping technology. Businesses will also have to manage the way they pull large data sets.

Other issues that might crop up include managing latency, dealing with structural web page changes, and dynamically-generated content. There are also ethical considerations around using the technology, along with respecting the rights of website owners. It’s a good idea to learn about different issues that might arise and how to work through those challenges.

Rayobyte offers a variety of web proxy solutions designed to support your need to build a reliable and efficient web scraping infrastructure. Contact us today to learn more about the products we offer.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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