You have met with the information, and know that it is the real power, right? Let's say you are using an app and you need to feed the app with the data. But where is this data come from?
You are scraping data from various websites. And, that is what web scraping is. At first, you may scrape data from a number of websites, let's say 30, but when the time goes on the number of websites that you scrape data will increase in accordance with your needs, too. Well, it might not be that easy to scrape data from nearly 700 or 800 websites. It is time to scale up the process!
However, scaling up the process can be bothering sometimes. You may encounter lots of problems in this process, and they are even not the same as what you encountered at first! No worries, we are here to tell you the 9 web scraping challenges you should know.
It is inevitable to have some troubles when it comes to scaling up the business. Think web scraping at a large scale as a business, there could be lots of obstacles that roadblock the growth. When it comes to scraping data at a large scale bots' blocking mechanisms might become a part of the activity.
Before we begin, let's see main things to check before starting web scraping!
Let's examine the 9 challenges:
Data Warehousing and Data Management
It is not a secret that large-scale web scraping produces a massive amount of data. Let's continue, if you are part of a crowd team, many of your colleagues will be working on the data. Of course, this does not point to an issue. However, that is a factor that might create a problem that companies generally overlooked.
You need to build a proper and problem-free Data Warehousing infrastructure. Otherwise, you may face problems such as querying, searching, filtering, and exporting data at a time when the web scraping process has reached a large scale. In addition to these, the Data Warehousing infrastructure has to be scalable, fault-tolerant, and secure to maintain data scraping and extraction in the most perfect way.
Website owners want to attract visitors' attention and improve their user experiences. That's why they need to improve their digital experience, and in order to provide this, they periodically upgrade their User Interface.
That's why scrapers need adjustments for a period of time cause even a minor change in the website affects the quality of data.
Some websites use powerful technologies that block web scraping attempts. LinkedIn, which we are all familiar with, is a good example. Such websites use different algorithms and encodings to disallow bot access and implement IP blocking mechanisms, even if it does not cause problems for legitimate web scraping practices.
Of course, there is a solution to this problem, but you may have to spend a lot of time and money to produce a technical solution. Many companies develop and use software that mimics human behavior to fool anti-scraping technologies.
IP Based Blocking
An IP address is your identity on the internet. In other words, we can say that an IP address is a special address assigned to a device.
For example, big websites like Amazon can block your IP network. You know that there are thousands of products on Amazon, and let's say the price data of these products are important to you. Let's say you create a simple web scraper to scrape product prices. In the first scraping you will get few results, but then the scraper will fail. This is because Amazon blocks your network's IP. Many websites can block web scrapers' IP addresses if the number of requests from an IP is above a threshold. But this problem usually has an easy solution. You can avoid this problem by using a reliable proxy service that works at scale.
CAPTCHA Based Blocking
CAPTCHA is one of the toughest anti-scrap apps you can come across. It does not approve the pass like a security guard! Many e-commerce companies that do business online use CAPTCHA to identify non-human bot behavior on their website and naturally disallow scraping. It is very likely that you will encounter CAPTCHA at some point. While CAPTCHA solvers can help solve this problem, they will still reduce your scraping speed.
Some web designers place honeypot traps inside their websites to detect and trap web scrapers or bots. These traps are links that normal users cannot see and that a bot can. Just as a bee sees honey when it flies to a flower, and we humans only see a beautiful flower.
The scraper must be capable of handling honeypot traps and must be carefully designed.
Quality of Data
You need to make sure that the data meets the quality guidelines because web scraping must be performed in real-time. Therefore, continuous monitoring is critical. In addition, the quality assurance system needs to be kept up to date and checked for new cases. A quality control system alone is not enough to keep the verified data of good quality. Instead, you need a solid AI layer.
One of the potential risks of large-scale scraping is that it is illegal. Scraping means more requests per second. This could also be misinterpreted as a DDoS attack in a courtroom. There is no legal limit to the rate of web scraping in the US, but if the scraping overloads the server, the person responsible for the damage may be prosecuted under the "trespass to chattels" law.
In Europe, however, the legal issue you have to worry about is GDPR from here. GDPR is software that prevents companies from using personally identifiable information. However, in a large-scale and detailed scraping process, any personal information is likely to leak.