Earlier we all were depending on Printed media to get the news, later TV and nowadays websites, and other social Medias. And most of the news Channels, Newspapers had their own news websites from which we all can read, grasp the daily happenings in the world and post your opinions live. All of us in various shapes and forms would use the website. So most of us todays times we read about news from websites right. It might be any of the websites like, BBC .com, or it can be National Website called Times of India or NDTV .com or

In this newspapersare the websites we get to see is many of the international articles, are the articles which are not local for a given website. At the end, the article get to see that the news has been sourced, the external means and our team has only changed and edited the headlines.

Which means the source for this particular news content is not driven by the particular website, it has been sourced globally from website.Traditionally the way the newspapers were being operated was there was a central hub where the editor used to edit all the content. News Editor get the news/journal from the journalist from the field who research and report the news content. When this news reported at the editor, the editor takes a call whether to publish?If to publish-how to make the changes and publish the content on a given newspaper. Now with changing times, every newspaper or every publishing company cannot have journalist globally. And people today need local news to global news. So when we say this humongous scale of local, national and international it’s not easy to scale up journalist to have all the places. Hence the new approach to source content from the internet and sourcing of data from the internet called as Web crawling(moving into non – named direction)

Why Web crawling to be discussed?
The reason is once we do Web crawling we get the source where the news is available.For e.g.:- While India sleeps, the US is active and vice versa. There are various activities in a day and if at all the news has to be reported in ‘real time’ then the media houses are using this web crawlers. So the web crawlers are yourcontent which are fed with various keywords and this keywords are like your continues google searches which are searching the internet for any news which gets published in the internet at the any point of the time. And the moment a news is published then this web crawlers report to the editor, and the editor takes a call whether the news has to be reported or it’s an information for him.
Once he decides what news to be published then it is up to the editor go head and grab that news. So when we say grabbing the news we just can do traditionally by going into the host website and copying the news and then pasting it where ever required format. But can it can be done manually every day?It is something very tedious. To do such kind of problems or to understand such kind of problems there is a functionalities which we called us Web Scraping.

The quotient of identifying the source –Web crawling.

Extracting data from source and storing in repository-Web Scraping.

In an engine perspective, Web Scraping is a technique employed to extract large amount of data from websites whereby the data is extracted and saved to a local file in your computer or to a database. So there is a website which is the source website then you a tool to copy that data and then you have a destination where you can save it your XML file, HTML file or CSV file, or data can be fed to into a database which is your destination location.
In totality it is a series of steps of targeting, copying and storing of data.
Four function to see in Web Scraping

Transformation is because the data at source might be in HTML format or it can be in other format but that data which is sourced has to be copied, transformed and stored before it can be reduced.
In python, the libraries which helps you to do Web Scraping are

Of all the 5 libraries, the one which is heavily used is Beautiful Soup and Requests.
Both this module has such functionality to transform the data from HTML and store the data in whatever form we need to do and take it further.
Beautiful Soup that helps to do scrap the content and put the data in your system.

Requests were it will help you to target a URL(http URL) and then report on that once you have that URL, then you can use the Beautiful Soup then copy the content into whatever shape and from you need to do that.

Using one of the two strong libraries within the python space which is Beautiful Soup and Requests and continuously we can keep running is in the loop to grab the data if it’s a continuous data.
For e.g.: Wikipedia web page is a static page and but if you have to do it from a stock market pricing or in a sports perspective, movie review perspective, the entire engagement into a loop and get our data going on.


For Marketing: Lead Generation

A web scraper can be used to gather contact details of businesses or individuals from websites like or Here we can get email address, phone, website URL etc. that are extracted using a web scraper.
For Businesses / ecommerce: Market Analysis, Price Comparison, and Competition Monitoring

Web scraper application can be used to identify the typical products and services related to specific domain in a market.

Artificial Intelligence & Machine learning

Web scraping is used in most data science application, where the large cooperate companies restrict their data to public. Scraping is more about cleaning and structuring data that is grabbed, thus a set of job erase termed as data grabbers who need to up scaling their skills every day.

Sentiment Recognition

Mainly done by scraping data from twitter or other forums with comment sections. Like on an election day, a machine, predicts results with even a moderate accuracy, who’s going to win, by analyzing the mood of people, by going through their tweets

Research- Researches in laboratories mainly depending on laptops and Macbooks through web scrapping instead of huge apparatus and machines.

Other Industries which are Using Web Scraping

News and Reputation Monitoring – Web scrapping helps to allow quick and efficient way of passing data and news varying from individuals to companies whereas the data’s shall be tracked.
Academic – Web scrapping helped students to extract and process data that they need where there is a teaching assignment or a research project.
Data Journalism-Web scraping helps a journalist to get the data at first place, which help him to think more creatively by using the available data
Employment – Using you can scrap job postings and understand history of potential candidates through job notices, job descriptions etc. which will help to connect with potential job seekers and employee profiles.
Search engine for classified sites–using web scraping applications you can search classifieds with its specific details which will help to find the right thing he need.


With the rise in Data,redundancies in web scraping increasing, collaborative could be a common one in future where client gets customized scrapping tools.Another major concerns is on the privacy of people and firms as the data’s are freely available which can lead to target marketing and other negative impacts. Since most of people in world use internet from the mobile availability of data’s are on your fingertips in seconds.
Future of web scraping is bright. The reason why Scraping will be always demand in future:-

o People/business always love to gather data instantly, no manual efforts.
o Competitor’s in business – web scraping helps a lot in it to gather data from competitor’s website.
o In Marketing- there is always need of database of targeted audience.
o We are very fast in the field of technologies, soonthere will be more advanced web scraping tools, and additional services like proxy servicesand this, which will lead to get more opportunities in web scraping.

• The ‘big data’ can be both, structured & unstructured; web scraping tools will get sharper and incisive. There will be competition between those who provide web scraping solutions. With the evolution of open-source languages like Python, R & Ruby, Customized scraping tools will only establish bringing in a new wave of data collection and aggregation methods.
• Companies need to engage their team to capitalize the opportunities in the application of Web scraping. In future Web scraping in compulsory in business as you may be able to handle the data manually.

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