Web scraping or web harvesting needs a reliable tool to be successful. This involves data crawling, data fetching, searching, and parsing. It also includes data reformatting that makes the collected data available for analysis and presentation. It is essential to choose the correct software and languages for web scraping.
Below are five of the most popular programming languages for web scraping. The list was compiled based on several factors, including ease of use, intuitiveness, code quality, maintainability, flexibility, and effectiveness in web scraping. The popularity of the software is also important. Popular tools tend to be more up-to-date and supported by a larger community of users, who can assist each other in solving problems or learning new ways of web scraping.
1. Web scraping with Python
Python is the most popular programming language for web scraping. According to IEEE Spectrum, Python is the most popular programming language for web scraping. This object-oriented programming language includes a large number of libraries, including modules that allow for machine learning.
Python is the best choice for web scraping because it can handle almost all data extraction processes. Python’s ease of use (non-usage curly braces and semicolons in particular) is also a plus. Python can be used directly to create variables whenever necessary. This makes it much easier and quicker to do the job. Programming language is also well-known for its “small code and big task” approach. Codes are typically smaller than those of other programs.
Python syntax is also very simple to understand. It’s similar to reading English sentences and phrases. Even beginners to programming with Python, even those with no knowledge of the subject, will be able to understand or have an inkling of what the codes do.
This is also a benefit of Python’s large global user base. There are numerous discussion boards and chat rooms dedicated to Python programming. It is easy to find advice and help when you are having trouble writing your web harvesting program.
Also read: Top 10 Python Frameworks for Web Development
2. Web scraping with Ruby
Ruby is another popular programming language used for web scraping. Ruby is a popular programming language for web scraping. Its simplicity and clear syntax are great for all levels of coders. It’s also known for its productivity.
This programming language is great for production deployments. Ruby’s string manipulation is based on Perl syntax. This syntax is great for web page analysis and is easy to use.
Nokogiri is Ruby a preferred web scraping programming language, that is described as being easier to use compared to Python. that is a simpler way to deal with broken HTML/ HTML fragments. Web scraping with Ruby is possible with the help of popular Ruby extensions like Loofah or Sanitize especially when it comes to addressing broken HTML can be very simple and smooth.
Ruby is a better choice than Python when it comes to cloud development and deployment. The Ruby Bundler system, which is amazingly efficient in managing and deploying packages from GitHub, is the main reason.
Ruby also has great testing frameworks which simplify and accelerate the creation of unit tests. These include advanced features such as web crawling with WebKit/selenium, one of the most widely used open-source tools to automate web applications.
Node.js’s unique feature is the way it is processed by computers. Each Node.js process can be handled by one CPU core. Multiple instances of the same script are possible on modern devices with multi-core CPUs.
Node.js can be used for web scraping. It is not the best choice for large data harvesting. It is not recommended for long-term processing.
4. Web scraping with C++
C++ has a lot of associations with general-purpose programs, but it is also a great language for web scraping This object-oriented programming language features data abstraction, classes, and inheritance. These qualities make it possible to reuse and repurpose code for other purposes. The language’s object-oriented nature allows for easy storage and parsing.
C++ is also known for its high scalability. C++ can be used to create smaller projects and reuse the code with some modifications or tweaks. C++ is static and will not work in situations where dynamic languages are needed.
C++ is also not a good programming language for creating web crawlers. C++ is a great programming language for web scraping. However, it’s not the best choice for creating URL lists or other crawling activities.
C++ remains a popular programming language. When you run into coding difficulties, it is easy to get help from C++ coders. Many developers are willing to share their knowledge in different forums and groups.
Also read: Top 10 Web Scraping Proxies
5. Web scraping with Java
Java is still one of the most popular programming languages on the globe. It is the most popular programming language in the index. It is not surprising that it is the preferred programming language for web scraping.
Java provides a wide range of libraries and tools that Java can use to build web scrapers like JSoup, HTMLUnit, and Jaunt. JSoup is a simple library that allows data extraction and manipulation via DOM traversal or CSS selection. HTMLUnit allows you to simulate web page events like clicks and submissions. Jaunt, meanwhile, is a library dedicated to web automation. It can be used to scrape data from HTML pages or JSON data payloads.
Java is not the best choice for advanced web scraping projects. It does however support the creation of web scrapers that can be used for different purposes. It is used by a vast majority of businesses around the world, so there is a large user base from which new developers and inexperienced users can seek advice or help with resolving issues.
Combining web scraping with automated tools
These languages allow anyone to do web scraping projects using the five programming languages. However, they are not appropriate for all users and projects. It is crucial to research the best programming language for your project based on clearly defined objectives.
Automated solutions are available to companies that want to web scrape at scale. Bright Data can, for instance, run millions of web scrapers simultaneously, which helps with scaling web data collection projects. It provides a broad proxy infrastructure to address the issues of website data harvesting.