💡 What is Web Scraping?
Web scraping, also known as data scraping, is the process of extracting and collecting data from the internet or websites. The scraped data can be saved on a local system or can be utilised for data analysis.
Web scraping, for example, is similar to copying data off the internet and pasting it into a spreadsheet on a small scale. On a broad scale, data on the web is primarily in an unstructured format (i.e., not in the form of tables), and we must convert the unstructured data into a structured format (i.e., tables or CSV files).
Scraping data from the web may be accomplished in two ways: first, with a programming language (most often python), and second, with a Web scraper bot or software application.
📌 What types of Data can be scraped?
If there is information on a website that is in the form of a theory or paragraphs, It’s too scrapable. Data on the internet may take numerous forms, including photos, videos, text, and numbers, and it is possible to extract a wealth of information, including comments, reviews, product information, forums, stock prices, and pricing for various services.
📌 How is Web Scraping useful?
Web scraping has a wide range of applications, most of which are in the field of data analysis. Web scraping is used by large corporations to extract data for sentiment research on social media, brand product information, stock and financial analysis, and competitive analysis.
Many sports teams do this to get vital information on their players and their performance to assist them to perform better in their next game. Even Google collects index and rank data using web scraping technology to see how well their services are functioning.
However, on the dark side, this technology is being misused by certain unscrupulous actors in the market, who are harvesting sensitive data from the internet, such as personal information and bank account numbers, to initiate fraudulent contact.
💡 Types of Scrapers
Web scrapers can be classified into a variety of categories, including pre-built scrapers, browser extensions, computer applications, and even cloud-based scrapers.
📌 Pre-built web scraper
It can be challenging, but not impossible, to be a pre-built or self-built web scraper. Such a role necessitates extensive and in-depth knowledge of programming languages, as well as exceptional problem-solving abilities.
Although there is a huge range of tools and languages you may need to master, there are specifics about some of the languages required to web scrape programmatically in this blog. The road to becoming a pre-built scarper might be tedious and time-consuming, but persistence and patience will take your talents to the next level, allowing you to stand out from the crowd.
📌 Browser Extension and Computer Application
Because they are integrated with your web browser, browser extensions can be simple to use. The only drawback is that they may be less flexible, and advanced scraping services may require in-app fees or subscriptions. On the other side, software or applications can provide all of the functions that the browser extension does not. You only need to install it on your local system and start using it; while it can be difficult to learn, practising every day can make your work go more smoothly.
📌 Cloud server
Cloud servers, as their name implies, are located in the cloud. This allows your machine to perform other tasks because the web scraping process is performed on your local machine rather than in the cloud, which is beneficial because it does not require your machine’s RAM or other specifications, and web scraping can be handled by cloud server systems and run smoothly.
💡 Web Scraping toolbox
📌 Python
This section of the blog may be beneficial if you want to become a pre-built or self-built web scraper; otherwise, you may skip it.
So, with all of these readily available and helpful tools to make your job easier, why would anyone need a web scraper who has to go through the difficult process of learning a programming language and extracting data with it? The answer is simple: self-built scrapers can highly personalise the requirement to extract data where most software fails, and many firms hire self-built web scrapers, thus this will continue to help you in your web scraping career in the future.
Python is the most extensively used programming language for web scraping, and Python is now topping the IT market as a demanding programming language. Many libraries for web scraping have been created and can be coded in Python. Beautiful Soup is a highly regarded library that extracts data from a webpage using HTML tags. Selenium is another library that aids in the extraction of data from a dynamic website where data is constantly changing or updated (Youtube, Facebook, Instagram). Pandas library can be used to manipulate data as well as create and store data files on a computer. Scrapy is another framework that is detailed in full further down.
📌 Scrapy
Scrapy is a Python-based open-source online scraping toolkit for creating web scrapers. It provides you with all of the tools you’ll need to rapidly extract data from websites, process it, and store it in the structure and format of your choice. It is developed on top of the Twisted asynchronous networking technology, which is one of its key features. If you have a huge data scraping project and want to make it as efficient as possible while maintaining a lot of flexibility, this data scraping tool is a must-have. Data can be exported in JSON, CSV, or XML formats. Scrapy is notable for its ease of use, extensive documentation, and active community. It’s compatible with Linux, Mac OS X, and Windows.
📌 ParseHub
ParseHub is a web-based data scraping application that supports JavaScript, AJAX, cookies, sessions, and redirects when crawling single and many websites. The programme can evaluate and extract data from websites, then transform it into useful information. It recognises the most complex documents using machine learning technology and provides an output file in JSON, CSV, Google Sheets, or via API.
📌 Diffbot
The Diffbot application allows you to set up crawlers that can go into websites and index them, then process them using Diffbot’s automatic APIs for data extraction from varied online content. If the automatic data extraction API doesn’t work for the websites you need, you can develop your own extractor. Data can be exported in CSV, JSON, or Excel formats.
Some Honorable Mentions:
💡 Summary
We’ve looked at what web scraping is, how it’s done, what it’s used for, and the different sorts of web scrapers and their tools in this blog.
Web scraping can be used to capture a wide range of data kinds, including images, videos, text, and numerical data.
Web scraping can be used for a variety of purposes: The possibilities are boundless, from contact scraping to scouring social media for brand mentions to performing SEO assessments.
Web scrapers come in a variety of shapes and sizes, and it’s important to know what each one does and how it works.
Scrappers that have been pre-built must meet the following criteria: Why are corporations still looking for self-built scrappers instead of software-based scrappers?
Python is a popular web scraping language: Beautifulsoup, scrappy, and pandas are all popular Python libraries for scraping the web.
Other software-based tools include Diffbot Parsehub and many others, which are utilised in a variety of data scraping applications.