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How Open Source Can Help You Scrape LinkedIn in a Postgres Database

“Data” is changing the face of our world. It might be part of a study helping to cure a disease, boost a company’s revenue, make a building more efficient, or drive ads that you keep seeing. To take advantage of data, the first step is to gather it and that’s where web scraping comes in.

This recipe teaches you how to easily build an automatic data scraping pipeline using open source technologies. In particular, you will be able to scrape user profiles on LinkedIn and move these profiles into a relational database such as PostgreSQL. You can then use this data to drive geo-specific marketing campaigns or raise awareness for a new product feature based on job titles.

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How to Deal With the Most Common Challenges in Web Scraping

Introduction

In the world of business, big data is key to competitors, customer preferences, and market trends. Therefore, web scraping is getting more and more popular. By using web scraping solutions, businesses get competitive advantages in the market. The reasons are many, but the most obvious are customer behavior research, price and product optimization, lead generation, and competitor monitoring. For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.

Modifications and Changes in Website Structure

From time to time, some websites are subject to structural changes or modifications to provide a better user experience. This may be a real challenge for scrapers, who may have been initially set up for certain designs. Hence, some changes will not allow them to work properly. Even in the case of a minor change, web scrapers need to be set up along with the web page changes. Such issues are resolved by constant monitoring and timely adjustments and set-ups.