The internet is the largest source of information to ever exist—arguably. Much of this information can be scraped and gainfully utilized. Web scraping, including tackling the toughest web data scraping challenges and how to overcome them, has numerous applications, including business and competitive intelligence, monitoring customer sentiments, and facilitating marketing strategies.
But effectively extracting data from the web is as difficult as they are useful. The sheer volume, variety, and velocity of data on the web make any systematic scraping challenging. There are other hurdles besides that are no less daunting and bugging.
Numerous solutions exist, too, thankfully. And they come in numerous forms. If you want all the solutions in a single package, then you can outsource data extraction services to a web scraping company that collects all the data you need and presents them to you in the format you want. If, however, you want to scrape web data yourself come hell or high water, then you’d do well to read on.
Most pressing data scraping challenges—and their solutions
Web scraping goes as follows. Identify the target website, analyze the website’s structure, send an HTTP request, and retrieve the data, including the toughest web data scraping challenges & how to overcome them. And then clean, transform, and store them for further usage.
A typical representation of how a web crawler scraps data | Source: GitHub
Straightforward it seems; the nitty-gritty of the process can be anything but—the devil’s in the details. The process is bedeviled by a number of challenges and constraints. The following are some of the most significant ones, and how you can tackle or avoid them.
Web structure changes
When websites’ structures change—and they do change periodically to update content, improve user experience, or incorporate new features—the web scraping process can be interrupted. An alteration in the website’s HTML structure can disrupt the way a web scraper identifies and extracts data elements. Changes in the class names for example may make it difficult for the scraper to identify them and extract relevant information. Similarly, a change in the URL structure can complicate finding the pages from which to scrape data.
Changes in the way information is presented or the format in which it is stored can also affect the quality and consistency of the data scraped. A scraper needs to be able to adapt to these changes as even a minor alteration in the website layout can interfere with the scraper and prevent it from returning the right information.
Solution: To stop structural modifications in websites from getting in the way of effective data extraction, utilize robust web scraping tools with mechanisms to detect and adapt to these changes. Use HTML parsing libraries that are adaptable and CSS selectors that are less sensitive to changes. These should minimize the need for manual intervention but not eliminate it. So you should monitor target websites’ changes and anti-bot measures. For this, you can create cron jobs that run at regular intervals and notify you whenever there has been a change.
Explore the possibility of using APIs or data feeds provided by the website. APIs are less prone to structural changes and offer more structured and up-to-date data in an accessible way.
Website administrators often have measures to deter activities that can put undue strain on their resources. IP blocking is one such measure. The website owners may block your scraper if it repeatedly makes several requests; they may have blacklisted certain regions that include yours; or they just dislike their content being scraped.
Whatever the reasons, IP blocks can make scraping web data difficult.
Solution: The obvious workaround for IP blocking is to use proxy servers to route requests through intermediate servers, effectively masking the real IP address. Choose and rotate proxies strategically to mimic diverse geographic locations and users. Frequently changing the User-Agent header to emulate different web browsers or devices also helps.
Less obvious but more important is to respect the target website’s guidelines and scrap its data ethically and legitimately. This entails adhering to the robots.text file and/or obtaining explicit permission for scraping data.
Adjust the scraping rate to stay within the limit specified. Distribute requests over time and region to avoid overwhelming the website’s server. Consider also using APIs to obtain data to reduce the need to bypass IP blockade.
CAPTCHA impedes web scraping. The clue is in the name, which stands for Completely Automated Public Turing test to tell Computers and Humans Apart. It is used to distinguish humans and automated bots and to protect websites from spam and bot raids. Web scrapers are merely automated bots.
Though CAPTCHAs are simple and though bots have become adept at solving them (better even than humans according to one study), they can significantly slow the process of web scraping.
Solution: Getting through the CAPTCHA is pretty straightforward: solve it. The best way around is through. Utilize CAPTCHA-solving services and integrate them into the scraping scripts. These often use machine learning algorithms to solve CAPTCHAs automatically. It is important to ensure that the services you use follow the target website’s guidelines, as some websites disallow their employment expressly. Adhering to them can also reduce the chance of triggering CAPTCHAs in the first place.
Another effective workaround is to use a headless browser. This allows you to automate signup for websites with CAPTCHA and quickly navigate websites and collect data. You can use open-sourced headless browsers like Puppeteer, Selenium, HtmlUnit, and Playwright.
Dynamic content loading
Handling large data sets
When scraping the web, you will often find yourself flooded with a barrage of data. The scraper needs to be able to handle network bandwidth limitations, manage concurrent streams of data, and maintain the integrity of the data.
And as the size of the extracted data grows the web scraping process becomes more demanding and prone to errors. Handling the data properly is thus essential. Otherwise, the whole process of web scraping is rendered futile.
Solution: First, to reduce the strain on computational resources, divide the scraping process across multiple devices or servers to distribute the workload. This can reduce scraping time and resource consumption and also increase the scalability of extraction and handling of data. Secondly, for similar reasons, use distributed storage systems; and if possible, compress the extracted data to minimize storage requirements and reduce network bandwidth during transfer.
Web scraping is beset with several challenges, many of which are persistently nagging, including the toughest web data scraping challenges & how to overcome them. Thankfully, they are also not so intractable as to make extracting web data worthless—far from it. Solutions exist; one only needs to dig deeper to find them.
One solution is to have onboard a web scraping company and seek their service. Third-party companies can provide a complete suite of web data extraction services and enable you to reap the benefits of web scraping without getting yourself mired in the challenges. And they are cost-effective.