5 Best LinkedIn Scraping Tools (Ranked) by Scalability, Data Quality, and Pricing

If you have researched enough, you must by now know that there are tools with which you can scrape a few hundred profiles/jobs without any problem.

These tools either use your LinkedIn Profile/LinkedIn Sales Navigator which makes them limited in terms of their abilities to extract data.

The main problem arises when you scrape LinkedIn at scale. Some of them are ⬇️

1. Legal and Ethical Issues:
   
   
   • User Agreement Violation: LinkedIn’s terms of service prohibit the scraping of their website. Engaging in this activity can result in legal action against the perpetrators.
     
     
   • Privacy Concerns: Extracting user data without consent violates privacy norms and can lead to severe repercussions. I have discussed in detail about the legality of scraping LinkedIn in this blog.
     
     
2. Technical Challenges:
   
   
   • Rate Limits: LinkedIn monitors and restricts frequent and massive data requests. An IP address can be temporarily banned if it makes too many requests in a short period.
     
     
   • Dynamic Content Loading: LinkedIn uses AJAX and infinite scrolling to load content dynamically. Traditional scraping methods often fail to capture this kind of content.
     
     
   • Complex Website Structure: LinkedIn’s DOM structure is intricate, and elements might not have consistent class or ID names. This can make the scraping process unstable.
     
     
   • Captchas: LinkedIn employs captchas to deter automated bots, making scraping even more challenging.
     
     
   • Cookies and Sessions: Managing sessions and cookies is necessary to mimic a real user browsing pattern and avoid detection.
     
     
3. Maintenance Issues:
   
   
   • Frequent Changes: LinkedIn, like other modern web platforms, frequently changes its user interface and underlying code. This means scrapers need constant updating to remain functional.
     
     
   • Data Quality: Ensuring the scraped data’s accuracy, relevancy, and completeness can be challenging, especially at scale.
     
     
4. Infrastructure and Costs:
   
   
   • Large-scale Scraping: Scraping at scale requires a distributed system, proxy networks, and cloud infrastructure, increasing the complexity and costs.
     
     
   • Data Storage: Storing vast amounts of scraped data efficiently and securely is another challenge.
     
     
5. Anti-Scraping Mechanisms:
   
   
   • Sophisticated Detection: LinkedIn employs sophisticated bot detection mechanisms. Mimicking human-like behavior becomes essential to avoid detection.
     
     
   • Continuous Monitoring: LinkedIn monitors for suspicious activities and can block accounts or IP addresses even if you successfully scrape data.

Brightdata, along with its large proxy network also provides LinkedIn Scrapers.

Scalability

Their scalability is great but the error rate might go up due to disturbances in their proxy pool. Bright data goes down frequently.

Pricing

Their solution could be a little expensive. The per-profile cost is around $0.05.

Developer Friendly

The whole documentation is quite easy to read and there are request builders which can help you get started quickly.

Stability

No doubt Brightdata has the biggest proxy pool in the market but they frequently go down sometimes. But overall their infrastructure is quite solid.

Data Quality

They do scrape fresh data like Scrapingdog.

Read More: 5 Best Bright Data Alternatives for Web Scraping