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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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Don’t Confuse Business Process Management with Network Automation

It is not uncommon for enterprises to confuse the role of a Business Process Management (BPM) system with that of a network automation solution. Because there are a lot of similarities between the two, we often see organizations attempt to leverage BPM systems to automate network activities in an effort to leverage their existing investments in these tools. Additionally, the allure of open source BPM options is often mistakenly seen as low-cost alternatives to network automation solutions. While the differences between the two are vast at their core, they are often overlooked or discounted in the planning process due to their common traits and similarities at the surface. Inevitably, their differences always present themselves as major problems during implementation when operators discover they can’t force a network focused automation process into a system not designed with that in mind; it’s just not the right tool for the job and as a result, falls short in automating critical network operations.

When looking at the capabilities of a BPM system as compared to a network automation solution, they both share similar features such as: