You may be familiar with the concept of human language grammar, syntax, and interpretation. The same principles apply when we are talking about a computer language with one fundamental difference: here you need to be understood by the machine and its configuration, so that your commands would be “interpreted” properly to get the expected or desired results on the output.
With over 4 billion million users worldwide, social media platforms have become a lucrative data tidbit for market analysts, recruitment executives and business owners around the planet. This fact dramatically increased the popularity of all types of data scraping on Facebook, Twitter, Instagram and Linkedin: bots and automated scrapers crawl the social media for geo-targeted info on businesses, prospect candidates, customers and decision makers in all possible areas. But is it all legal in the first place? And how can you maintain ethical standards while automating your process of gathering publicly available data from social media platforms?
ScrapeBox is a must-have tool for all those involved in SEO related activities. It claims to be the “Swiss Army Knife of SEO experts” and deservingly so.
Crawling and web scraping a site without getting easily detected or blocked can be extremely challenging. If you are running a web scraping mission for gathering proxies for price tracking data for your business purposes, you might want to keep in mind a handful of useful tips on how to prevent getting blacklisted while scraping.
A lot of times people tend to confuse web crawling with scraping. And, although these two activities are somewhat similar in nature there is still a significant difference between them that deserves an article in our blog to explain it in detail.
Any business depends on reliable leads. Qualified leads are essential for generating sales and therefore revenue for any company. Marketing departments everywhere are busy constantly updating their databases of prospective clients that may generate future sales. Such databases normally contain all essential information about companies (location of offices, the contact information of decision-makers, etc.), including the likelihood of dealing with the prospects in the future.
You may be familiar with the concept of human language grammar, syntax, and interpretation. The same principles apply when we are talking about a computer language with one fundamental difference: here you need to be understood by the machine and its configuration, so that your commands would be “interpreted” properly to get the expected or desired results on the output.