Web scraping is traversing the Internet and collecting the data that is present on the web pages. It is sometimes called as screen scraping or web data extraction. The data extracted is saved to a local file in a computer or to a database in tabular form.
Data presented in almost all web sites can be seen only through a web browser. A copy of this data cannot be saved for any personal use. The other alternative is copying and pasting the data manually which is cumbersome and time consuming. A web scraping software or service automates the process. The data is copied from the web sites and saved, in the blink of an eye.
Web crawlers and scrapers work continuously to present data in an organized form. Most of the businesses today depend on web scraping services to extract data from various sources, which will otherwise consume too much of time, money and other resources.
Special data scrapers are capable of extracting raw and analytical data in the form of text, images files, etc.
Web scraping can be achieved in 2 different ways:
Web scraping consists of modules and components as follows:
Crawling – This is the beginning of the process and crawls sites for other related links. This is similar to browsing.
Scraping – The actual process which collects the data is scraping. It is similar to selecting a piece of information and copying it on to the clipboard.
Extracting – This process makes the data meaningful and structured.
Formatting – The extracted data has to be presented in an understandable format.
Exporting – After all the processes are completed, the data has to be exported or delivered to the consumer. This can be done through an API.
The internet has all kinds of data in it which includes text, media and data in any format. The uses of scraping in businesses and for personal use are many. Some of the most frequently used scenarios are:
A detailed research is carried out to accumulate all the details of sports. This is to be done with the help of event calendars.
How it is done: The latest information relating to all sports events which are conducted in a particular area are taken. This information is available online.
The data is collected from numerous web sources, so that the collected data is the latest one and also dependable. The data is transformed and saved into excel files.
The project also involves cleaning the data from the client on a regular basis, like weekly one. This data which is cleansed is then uploaded on the client’s web site.
Data is collected and analyzed from a number of sources of particular categories. The categories can be marketing, real estate, business, electronic devices, etc. The multiple sources present the data in as many multiple formats. Even if it is a single web site, not all data can be seen in one shot since it may cover entire work sheets or pages.
A web scraper in such instance, extracts data to a single source (like database or work sheet) making it user friendly for viewing and analyzing.
Any kind of research, academic or scientific becomes easier with a web scraper which collects data from hundreds of sources and organizes it in one certain way.
Lead generation using web scraper services has never been so easy. All the information can conveniently be sorted into categories like mail address, phone, web address, etc.
Job portals frequently scrape to collect data in one single place. They scrape company web sites to come up with a central job site that shows a list of organizations that are presently hiring employees.
The other areas of expertise where web scraping services are being used include:
Did you know that there is one more, not so popular application of web scraping? Yes, we are talking about Internet of Things (IoT). As the world is becoming increasingly connected, there is a plethora of data running back and forth between connected devices, servers, actuators, and the low-powered long life sensor devices.
At the heart of the IoT system’s success is the transfer of data that happens between different points passing through infrastructure like network cables, servers, storage, routers, network operations centres, device interfaces, and middleware. The IoT ecosystem comprises of hardware (Bluetooth sensors, smart home connectivity devices, routers, and Wi-Fi), infrastructure (as mentioned above), and application interfaces (like mobile devices, laptops, and servers).
With data scraping, the infrastructure gets the right kind of data at the right time to analyze and then pass it on to the application interfaces. It allows stakeholders to answer critical queries like what type of data is worth storing and assessing, what data to relay immediately, and what data needs to be transmitted for a long time to make sensible analysis and deductions.
The advantages offered by traditional data scraping become just a tip of the iceberg in an expanded IoT ecosystem. By crawling data across hardware devices, their interfaces, and the different connectivity points, it can present huge opportunities for insightful data analytics in IoT.
What are your thoughts about the value of data scraping in IoT? Do write to us and let us know.