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 app using data scraped from a large 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
Here are a few problems encountered while undergoing Data Extraction at large:
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. The Data Warehousing infrastructure needs to be scalable, perfectly fault-tolerant and secure.
2. Pattern Changes
Each website periodically changes its UI now and then. So should web scrapers. 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 will use anti-scraping technologies. LinkedIn is a good example for this. It takes a lot of time and money developing a technical solution that can work around 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 color disguised to blend in with the page’s background color.
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.