Large-scale web scraping shows challenges. These include large-scale scrape blocking systems. Continue reading for more web scraping challenges.
Market trends, client preferences, and rivals’ activities are analyzed using data collected by companies. Scraping can also be used for prospecting, marketing research, and other purposes.
Web scraping is another way to obtain data; it is a business strategy. In the near term, knowing just one data-gathering technique addresses the issue. However, each method has its benefits and disadvantages.
Finding innovative techniques to scrape web pages saves time and helps address the issue more quickly. However, collecting this data is complicated by online scraping issues.
The Challenges of Web Scraping
Scraping the web is not as simple as some people claim. Many challenges must be overcome before the data can be extracted appropriately. Here are a few challenges to keep in mind while scraping the web.
Bot Access
It is one of the most well-known online data scraping exercises. Websites have the option of allowing bots to access data. Automated data collecting is banned on several websites. However, the grounds for a prohibition might vary considerably.
Fair play principles dictate that you should always contact the website owner for permission before collecting data from a site that has a robots.txt file that prevents this practice. However, APIs can resolve these types of issues. For example, a well-designed web scraping API would resolve these restrictions without detection and get the needed results.
Captcha
Again, this is one of the most uncommon and challenging scraping difficulties. To discriminate between a human and a robot, CAPTCHA is used. Robots cannot perform logical problems or enter characters fast, but humans can. Indeed, bots are currently using CAPTCHA solvers to acquire data regularly. It slows down the procedure a little, but it is worth it.
IP Blocking
When dealing with parsers, IP blocking is one of the few options. As a result, it is also the quickest. A search robot can send many simultaneous queries to the server, resulting in a block. Geolocation-based IP filtering is also available. The site cannot collect data from a particular place. After that, the website will either prohibit or restrict access to the IP address in question.
Honeypot Traps
Website owners can capture parsers by placing honeypot traps on their sites. People do not notice the traps, but a parser can. The website can use the information it obtains when a parser falls into a trap to stop bots. For example, a “display: none” CSS rule or masked color to match the page’s background color can be found in certain traps.
Unstable or Slow Loading Speed
Websites can become unusable if they get a large number of visitors simultaneously. Waiting for the site to come back up can be done by refreshing the page. However, data collection can be disrupted if the parser does not know how to deal with this circumstance.
Web Page Structure
When scraping a website, you will have to deal with another obstacle. Designers can adhere to design guidelines when building web pages, leading to a wide range of page architectures. Websites, too, undergo regular updates to enhance user experience or introduce new features.
As a consequence, the website’s design often undergoes revisions. Web parsers, for example, are designed with page code components in mind, making the codes more complicated, which affects how the parsers perform.
As a result, they will not operate on the upgraded website since they were designed for a particular page design. Even a tiny modification might need a new parser setup at times.
Login Requirements
To receive information, you may first need to log in. This is because cookies are added to requests that execute on other websites after submitting your login credentials to the browser. As a result, the website will know that you are the same user who just signed in.
Login is not a problem but a step in the data collecting process. For this reason, cookies must be delivered with requests when gathering data from websites.
Real-time Data Scraping
For example, pricing comparisons, inventory monitoring, and so on all need real-time data collecting. Instantaneous changes in data can result in enormous profits for businesses. Because of this, a parser must constantly scan websites and gather data. But since parsers watch websites continually, it takes time to query and return data, and any instability might lead to errors.
Multiple Sources
The information can come from a variety of sources at times. For example, part of the data will be on the website, some in the mobile app, and some in PDF. However, the scraper is designed to gather all relevant data from a single source.
Consequently, it is difficult to gather and organize the data because some data can be missing entirely. Furthermore, the process is quite time-consuming.
Dynamic Content
It is a minor issue, but it should be brought up. Dynamic online content can be updated using AJAX. For example, when AJAX is activated, graphics can take longer to load, or more information can be shown by clicking a button. Parsers will not benefit from this method of seeing additional information on a page.
Hostile Environment
Ajax and JavaScript are two client-side technologies that can load dynamic content. However, web scraping becomes complex because of dynamic content production. It is hard to render JavaScript without a significant investment in technology. Therefore, scraping dynamic web pages that use Ajax and JavaScript is difficult.
Data Warehousing and Management
Web scraping creates a lot of data when done on a large scale. In addition, when working with a large group, the data will be used by many individuals. Because of this, an efficient method of handling data is necessary. Unfortunately, this is a point that most firms aiming to gather vast amounts of data fail to consider.
Querying, finding, filtering, and exporting this data will become tedious and time-consuming if the data warehousing infrastructure is not appropriately developed. As a result, large-scale data extraction requires a data warehousing infrastructure that is scalable, fault-tolerant, and secure. The quality of the data warehousing system might be a deal-breaker in certain business-critical situations when real-time processing is required.
Anti-Scraping Technologies
Certain websites actively use anti-scraping technologies to resist web scraping efforts. Even if one adheres to lawful online scraping procedures, such websites deploy dynamic coding algorithms to prevent bot access and install IP blocking measures.
Anti-scraping technologies need a lot of effort and money to build a technological solution. To get against anti-scraping systems, online scraping firms imitate human behavior.
Data Quality
Keeping data quality at a high level is a big problem when dealing with large amounts of data. The integrity of the data will be compromised if the records do not fulfill the quality standards. The last step is verifying that the data is of the required quality. On the other hand, web scraping is difficult since it must be done in real-time.
Quality assurance systems must be tested against new situations and certified regularly. To guarantee that quality is maintained at scale, you need a strong intelligence layer that learns from the data. Any machine learning or artificial intelligence effort that relies on data that contains errors is doomed to failure.
Anonymization Insufficiency
Anonymization is a must to safeguard your interests while scraping data in bulk. For instance, you are monitoring over a hundred e-commerce websites for competitors. To maintain anonymity, you will need a well-built proxy management system. The service provider you worked with at a smaller scale may not have the capacity to meet your needs. To avoid being sued, you must have a deficiency in your ability to remain anonymous.
Proxy Servers
Proxy servers are intermediary servers between the user and a website. It has an IP address. When a user uses a proxy server to visit a website, the website’s IP address is transmitted and received, then sent to the user. Web parsers use proxies to make the traffic seem normal.
Since the parser will be blocked if you do not have enough IP addresses, you can acquire IP address pools and distribute them randomly.
Distributing them using proxies is the most convenient method. However, the real IP address is obscured as the requests are routed via various IP addresses.
Data Accuracy
Inconsistent data may have a negative influence on data integrity. Keeping the data constant when crawling is difficult since it must be done in real-time. When using the most recent AI or ML technology, having inaccurate data might cause significant issues.
Conclusion
Many organizations nowadays rely on data. Therefore, even though the web scraping/web crawling method’s consistency and technological expertise are critical, you cannot ignore it. It is essential to work with experts who can swiftly build and include various beneficial features to get the most out of your web scraping efforts.
Web scraping will likely face further difficulties in the coming days. It is still important to treat web pages with care while scraping. You can also use IP rotation to improve web scraping performance. Good luck!