Web Scraping definition
A web scraWeb scraping is an activity in which we collect data about web pages where they appear in a form that is readable by humans and not optimized for machine processing. However, the output data is displayed in a structured form, typically in tabular form (eg Excel spreadsheet, SQL database). This allows data to be easily transformed, analyzed or otherwise used.
What can web scraping be used for?
Anything we may need for data collected from the web. Here are some examples of the many
One of the most common forms of use is common in e-commerce companies, who often want a database of product prices in online stores. This can be useful in helping with pricing and other data-driven decision making. Manufacturers often analyze the end-user prices of their products in a similar way.
In the case of companies selling to end users, online evaluations now play an important role in almost all cases. Therefore, an up-to-date database of monthly ratings is a great help to a wide variety of companies. It is possible to continuously examine customer satisfaction and notice both its significant change and the reasons for it.
Web scraping is also a regularly used tool in scientific research. Many studies are based on data collected from the Internet.
Lead generation, i.e. searching for potential customer data, is also a common use of the web scraping method. In this case, we often perform automatic data retrieval from company listing pages, Google Maps, or companies’ own websites, which can be used for both targeting (most likely converting customers) and contacting (e.g., phone number, email address). Although lead generation with business data is not particularly risky, the mass collection and use of personal data can very easily run into legality problems, especially in the European Union. Therefore, for example, we do not specifically deal with the collection of personal data, only company data.
In what cases is web scraping not a good choice?
While web scraping is a very useful tool to have, it is not universal. In many cases, especially with larger volumes of data collection, without custom development and good quality proxies, for example, success is unlikely, so costs can be high. In addition, because input data is unstructured and not readable by machines, individual handling of data errors and rare cases can consume a lot of resources.
Many websites explicitly block scrapers, so there is an ongoing cat-and-mouse battle between developers who bypass blocking and blocking algorithms. In addition, we did not even talk about the fact that in some cases we may run into legal problems (especially in the case of intellectual property and personal data).
So if the website whose data you want to process offers some direct data access (e.g. in downloadable files or with an API), it may seem costly in vain, this “direct” path is usually more cost-effective than using web scraping.
Is web scraping legal?
The process of scraping the web itself is always legal. Use of downloaded content may be subject to restrictions, such as copyrighted content and personal information.
Generally speaking, if you want to be legally secure, you should only use the web scraping tool for publicly available content that is not protected and does not contain personal information. An exception to this is if the subject has given his or her express consent.
How can we collect web data?
Web scraping can also be done manually with a simple “Ctrl-C, Ctrl-V” method. If we need very little data, it is often not worth talking to anyone else. Above one level, however, manual data collection becomes uneconomical. Plus, in most cases, people make more mistakes than automatic algorithms.
Perhaps the most common method of automatic web data collection is to use browser add-ons. The “Scraper” add-on (link) is often used in Google Chrome. Using add-ons is often the perfect solution when exporting the simplest data forms, such as one-page lists to an Excel spreadsheet. However, for complex or large-scale scraping tasks, it is almost certain that such simple tools will not work.
Downloadable, paid tools are even more sophisticated, which still allow web scraping without writing code, but can also be used for slightly larger data collections. An example is ParseHub. However, these tools are still limited in many respects, in terms of performance and thus often in terms of cost, outperforming custom development.
The last scraper development left. This is especially worthwhile if the target website is specifically complex (e.g., a lot of dynamic content) or has some sort of scraping protection (e.g., social media websites, larger marketplaces, and e-commerce websites). Above a certain project size (e.g., millions of rows of data every month), development is worth it in virtually any case, best because the one-time development cost is recouped many times over for better performance and thus less server and proxy costs.
What are the main difficulties we face when scraping the web?
Many times websites block scraping programs. Several service providers are developing complex AI-based systems specifically for this purpose, which detect patterns that are not specific to the user. The probable “robot” is then given either captcha tasks or the page is completely blocked. Two providers used by many websites are Datadome and Cloudflare. Although proper simulation of user behavior makes it possible to collect data from sites that are protected in this way, it often makes it difficult enough for many to no longer be able to access it.
Because websites usually have the sole purpose of allowing people to interpret the information, it is often more visually interpretable than processed by a program. This can make it difficult to create data processing programs. Similarly, web pages often modify details in the source code that cause a change in machine HTML processing that is not visually noticeable. In such cases, scraper programs should always be modified or reconfigured for visual “no-code” devices (such as downloadable devices or browser add-ons).
Web scraping is a very useful tool for businesses and researchers alike. In some cases, an additional 10 minutes may be enough to get the data you need. Above a certain complexity and amount of data, however, individual development is usually required.