We’ve done the Linkedin scraper that downloades the free study courses. They include text data, exercise files and 720HD videos. The code does not represent the pure Linkedin scraper, a business directory data extractor. Yet, you might grasp the main thoughts and useful techniques for your Linkedin scraper development.
Recently I was asked to help with the job of scraping company information from the Yellow Pages website using the ScreenScraper Chrome Extension. After working with this simple scraper, I decided to create a tutorial on how to use this Google Chrome Extension for scraping pages similar to this one. Hopefully, it will be useful to many of you.
In the post we share the practical implementation (code) of the Xing companies scrape project using Node.js, Puppeteer and the Apify library. The first post, describing the project objectives, algorithm and results, is available here.
On September 9th, 2019 the UNITED STATES COURT OF APPEALS 1 has affirmed the former district court’s determination that a certain [data] analyticcompany is lawful to scrape [perform automated gathering] LinkedIn’spublicprofilesinfo. Now the historical event has happened in which a court is protecting a data extractor’sright for mass gathering openly presented business directory information.
Recently I received this question: What are the best online resources to acquire data from?
The top sites for data scrape are data aggregators. Why are they top in data extraction? They are top because they provide the fullest, most comprehensive data [sets]. The data in them are highly categorized. Therefore you do not need to crawl and fetch other resources and then combine multiple-resource data.
Those sites fall into 2 categories:
Goods and services aggregators. Eg. AliExpress, Amazon, Craiglist.
Personal data and companies data aggregators. Eg. Linkedin, Xing, YellowPages. For such aggregators another name is business directories.
The first category of sites and services is quite wide-spread. These sites and services promote their goods with the goal of being well-known online, to have as many backlinks as possible to them.
The second category, the business directories, does not tend to reveal its data to the public. These directories rather promote their brand and give scraping bots minimum opportunity for data acquiring*.
Consider the following picture where a company’s data aggregator gives to the user only 2 input fields: what and where.
You can find more of how to scrape data aggregators in this post.
————– *You have to adhere to the ToS of each particular website/web service when you perform its data scraping.
The web scraping topic has been actively growing in popularity for dozens of years now. Freelance sites are overcrowded with orders connected with this contradictory data extracting process. Today we will combine two new and revolutionary directions in web development. So, let’s consider an elegant and modern way to scrape data from websites with Node.js!