You've built the prototype of an amazing application that gained some good early traction.
The application's core is data, and you're feeding the application data scraped from a small number of websites (say 20). The app turned out to be a big hit, and now it's time to scale up the data extraction process via web scraping (say 500 websites). However, scaling up becomes a rather tedious process. The issues that arise at this large scale are entirely different from what you've done at the early stages - this is a common challenge Datahut helps many companies overcome. When it comes to web scraping at a large scale, numerous roadblocks arise which can hinder the further growth of an application or organization. While companies may be able to do small-scale data extraction, challenges arise when they shift to large-scale extraction. These include combating blocking mechanisms that disallowed bots to scrape on a large scale.
Here are a few challenges people encounter while scraping at a large scale:
Data warehousing and data management
Let's face it web scraping at a scale generates a massive amount of data. If you're part of a big team - a lot of people will be using the data. Hence it would be best if you had an efficient way to handle the data. Sadly, this is a factor most companies attempting large-scale data extraction overlook.
If the Data Warehousing Infrastructure is not properly built, Querying, searching, filtering, and exporting this data will become a cumbersome and time-consuming. Therefore, the Data Warehousing infrastructure needs to be scalable, perfectly fault-tolerant, and secure for large-scale data extraction. In some business-critical cases where real-time processing is needed - the quality of the data warehousing system is a deal-breaker. From Snowflake to BigQuery - there are a ton of options available.
Websites Structure
Every website periodically upgrades its User Interface to increase user attractiveness and improve the digital experience. The changes are in the HTML code that holds the data that needs scraping. Web scrapers are built according to the HTML and Javascript code elements present on the website; the scrapers would require changes if it changes.
Web scrapers usually need adjustments every few weeks, as a minor change in the target website affecting the fields you scrape might either give you incomplete data or crash the scraper, depending on the logic of the scraper.
Anti-Scraping Technologies
Some websites actively use robust anti-scraping technologies which thwart any web scraping attempts. LinkedIn is an excellent example of this. Such websites employ dynamic coding algorithms to disallow bot access and implement IP blocking mechanisms even if one conforms to legal practices of web scraping.
It takes a lot of time and money to develop a technical solution to work around such Anti- Scraping Technologies. The companies that work in web scraping mimics human behavior to get around anti-scraping technologies.
IP based blocking
An IP or internet protocol address is a unique address that identifies a device on the internet or a local network.
Let's say you built a simple web scraper in Python to scrape the product prices from Amazon. When you run the scraper from your local system - you'll get a few results, but then the scraper will fail. The reason is Amazon blocking the IP of your network. Some websites like Amazon will block the IP addresses of the web scrapers if the number of requests coming from an IP is over a threshold value. Usually, the problem is solved using reliable proxy services that work at scale.
When you are sending many parallel requests or scraping real-time data, the chances of blocking are high.
CAPTCHA Based Blocking
E-commerce companies use CAPTCHA solutions (Completely Automated Public Turing test) to identify non-human behavior and block web scrapers. CAPTCHA is one of the most complex challenges of web scraping. At scale - you'll encounter CAPTCHA at one point or the other. Even though CAPTCHA solvers can help you get around it and resume the scraping process, they could still slow down the scraping.
Hostile environment, technology
There are some client-side technologies such as Ajax and Javascript for loading dynamic content. Dynamic content generation makes web scraping difficult. Rendering javascript is an extremely tough job, and without a significant investment in technology - it is nearly impossible. Ajax and Javascript are used to generate dynamic content - scraping such websites are complex.
Honeypot traps
Some web designers put honeypot traps inside websites to detect web scrapers or bots. They may be links that normal users can't see and a bot can. Some honeypot links to detect crawlers will have the CSS style "display: none" or color disguised to blend in with the page's background color.
The scraper should be carefully designed to deal with honeypot traps.
Quality of data
At scale, there is a significant risk in maintaining the quality of data. The records that do not meet the quality guidelines will affect the overall integrity of the data. Ensuring the data meets quality guidelines while web scraping is problematic because it needs to be performed in real-time. Constant monitoring is critical, and the quality assurance system needs to be checked against new cases and validated. Not just a linear quality checking system is not enough - you need a robust intelligence layer that learns from the data to ensure the quality is maintained at scale.
Faulty data can cause severe problems if you use any of the data as a base for any machine learning or Artificial intelligence projects.
Do you also face such challenges while scaling up your web scraping platform?
Legal Risks
One of the potential risks of scraping at a huge scale is it be illegal. Scraping at scale means more requests per second. High crawl rates can harm the servers of the website getting scraped. In a court of law, it could be misconstrued as a DDoS attack.
There is no rule against the legal limit of the rate of web scraping in federal courts' view in the United States. However, If data extraction overloads the server, the person responsible for the damage can be prosecuted under the "trespass to chattels" law (Dryer and Stockton 2013).
If you're in Europe, GDPR is another potential legal issue you should be concerned about. Due to privacy concerns, GDPR prevents companies from using PII or personally identifiable information. Usually, companies have filters in place to remove Personally identifiable information - however, at a huge scale and millions of records per day - there is a chance that a PII escapes your filter. What worked for you at a small scale might not work at a large scale. An example of PII is an E-mail address.
Anonymisation Deficit
When you're scraping data at a considerable scale, Anonymisation is a must to protect your interest. You could be doing Competitor monitoring across 100 eCommerce websites. You'll need a robust proxy management infrastructure to handle the anonymization. The provider you worked with at a small scale might not have enough resources to accommodate your requirements at scale. You can't have a deficit in your anonymization capabilities - if you do - you'll be exposing yourselves to lawsuits.
Final Thoughts
Web scraping at scale is a completely different ball game. If not done properly - it can expose you to lawsuits. You need a lot of human resources, money, and time to develop a system that extracts data at scale while maintaining anonymization. Get in touch with Datahut to combat your web scraping and data extraction challenges.
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