Web Scraping at Large: Data Extraction Challenges You Must Know
Updated: Feb 12
(Updated on May 2nd 2019)
You’ve built the prototype of an amazing application that gained some good early traction.
The core of the application 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 and the issues which arise at this large scale are entirely different from what
you’ve done at early stages.
This is a common challenge Datahut helps many companies overcome. When it comes to Data Extraction through 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 which disallowed bots to scrape on a large scale.
Here are a few problems encountered while undergoing Data Extraction at a large scale:
1. Data warehousing
Data extraction at a large scale will generate a huge volume of information. If the Data
Warehousing Infrastructure is not properly built. searching, filtering and exporting of this data
will become a cumbersome and time-consuming task. Therefore, for large scale data extraction, the Data Warehousing infrastructure needs to be scalable, perfectly fault-tolerant and secure.
2. Website Structure Changes
Each website periodically upgrades its UI to increase user attractiveness and improve the digital experience. This often leads to numerous structural changes on the website. Since web crawlers are set up according to the code elements present at that time on the website, the scrapers would require changes too. 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. Bad training data is the last thing you need to feed into your algorithm.
3. Anti- Scraping Technologies
Some websites actively use strong anti-scraping technologies which thwart any crawling attempts. LinkedIn is a good 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 developing a technical solution that can work around such Anti- Scraping Technologies.
4. Hostile environment/Technology
5. Honeypot traps
Some website designers put honeypot traps inside websites to detect web spiders, They may be links that normal user can’t see and a crawler can. Some honeypot links to detect crawlers will have the CSS style “display: none” or will be colour disguised to blend in with the page’s background colour.
6. Quality of data
The records which do not meet the quality guidelines will affect the overall integrity of the data. Making sure the data meets quality guideline while crawling is difficult because it needs to be performed real time. Faulty data can cause serious problems if you are using any ML or
AI technologies on top of data.