It has become a common practice for companies to acquire data for business applications. Companies are now looking for faster, better, and efficient techniques to extract data regularly. Unfortunately, scraping the web is highly technical, and it requires a pretty long time to master. The dynamic nature of the web is the main reason for the difficulty. Also, quite a good number of websites are dynamic websites, and they are extremely difficult to scrape.
Web Scraping Challenges
Challenges in web extraction stem from the fact that every website is unique because it is coded differently from all other websites. So, it is virtually impossible to write a single data scraping program that can extract data from multiple websites. In other words, you need a team of experienced programmers to code your web scraping application for every single target site. Coding your application for every website is not only tedious, but it is also costly, especially for organizations that require extraction of data from hundreds of sites periodically. As it is, web scraping is already a difficult task. The difficulty is further compounded if the target site is dynamic.
Some methods used for containing the difficulties of extracting data from dynamic websites have been outlined right below.
1. Configuration Of Proxies
The response of some websites depends on the Geographical location, operating system, browser, and device being used to access them. In other words, on those websites, the data that will be accessible to visitors based in Asia will be different from the content accessible to visitors from America. This kind of feature does not only confuse web crawlers, but it also makes crawling a little difficult for them because they need to figure out the exact version of crawling, and this instruction is usually not in their codes.
Sorting out the issue usually requires some manual work to know how many versions a particular website has and also to configure proxies to harvest data from a particular version. In addition, for sites that are location-specific, your data scraper will have to be deployed on a server that is based in the same location with the version of the target website
2. Browser Automation
This is suitable for websites with very complex dynamic codes. It is done by rendering all the page content using a browser. This technique is known as browser automation. Selenium can be used for this process because it has the ability to drive the browser from any programming language.
When content is rendered, it is saved locally, and the specified data points are extracted later. The only problem with this method is that it is prone to numerous errors.
3. Handling Post Requests
Some websites actually require certain user input before displaying the required data. For example, if you need information about restaurants in a particular geographical location, some websites may ask for the zip code of the required location before you have access to the required list of restaurants. This is usually difficult for crawlers because it requires user input. However, to take care of the problem, post requests can be crafted using the appropriate parameters for your scraping tool to get to the target page.
4. Manufacturing The JSON URL
Some web pages require AJAX calls to load and refresh their content. These pages are hard to scrape because the triggers of the JSON file can't be traced easily. So it requires manual testing and inspecting to identify the appropriate parameters. The solution is the manufacture of the required JSON URL with appropriate parameters.
In conclusion, dynamic web pages are very complicated to scrape so they require a high level of expertise, experience, and sophisticated infrastructure. However, some web scraping companies can handle it so you may need to hire a third party data scraping company.