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Best Web Scraping Programming Languages in 2024 with Stats

Today’s programming languages are very robust in supporting many use cases, including web scraping. The best programming language for a developer to build a web scraper is the language that they are familiar with. Web data often comes in complex formats and the structure of web pages can change frequently which requires developers to adjust their code. 

Familiarity with a programming language should be the main consideration since the scraping itself can be supported in almost any language. A second consideration that we mention throughout this article as well is the availability of online resources for solving a bug or looking up for alternative coding solutions for your problem. We summarized the pros and cons of some of three commonly used programming languages for web scraping and also low-code or no-code alternatives at the end of the article.

1- JavaScript

Most popular programming language in 2021 according to Github, JavaScript, was originally built for front-end web development. With the help of the Node.js environment, it is used a lot more for developing web applications as well. Node.js offers libraries such as Puppeteer and Nightmare which are commonly used for web scraping. 

According to a 2019 research comparing Python and Node.JS libraries for web scraping tasks, Puppeteer performed more efficiently than other options. We recommend using JavaScript with Node.js for web scraping to more experienced developers, especially if they have some experience with JavaScript.


  • Large community and availability of support through online forums and tutorials. Compared to Python and Ruby, which are the other two languages we cover in this article, JavaScript has the highest number of questions and therefore resources on Stackoverflow.
  • Node.js can handle concurrent web page queries at the same time very efficiently
  • For Input / Output (I/O) tasks which require a constant input and output flow, Node.js performs better than Python and minimizes the user’s wait time


  • Not easy to understand especially for less experienced developers
  • Not as robust and efficient as Python for CPU-heavy tasks, such as parsing large amounts of web data after collecting
  • The nature of JavaScript enables it to run multiple queries efficiently and introduces the challenge that is known as “callback hell” for JavaScript, which is simply a nested function structure that cascades an error happening in a function to other layers of the code. Less experienced developers in JavaScript should be aware of the work-arounds to avoid this challenge.

2- Python

Second most commonly used programming language of 2021 is Python, which is known for being an easy to understand language especially for beginner coders. It offers third party libraries such as BeautifulSoup and Scrapy specifically for web scraping as well as Selenium for automation of the web scraping task.

We recommend Python for web scraping for its ease of use and availability of resources such as open source guides and tutorials. If you are new to coding or web scraping, it will be quicker to get up to speed with Python, which is capable of completing any web scraping use case you may have.


  • Python follows JavaScript closely in terms of availability of online resources and community
  • For a less experienced coder, Python is a high level language and is easier to understand compared to other languages.
  • Python offers many useful libraries such as “pandas” for data wrangling or “re” for regular expressions which are very useful for parsing the web scraping output. It also offers specific data parsers for HTML and the specific “lxml” package for dealing with xml format which are needed for processing web data into a format that can be used for business analysis.


  • Python’s known challenges are not specific to web scraping but more general challenges of the language. One of them is database access. Python is known for weaker protocols in database access compared to JDBC or ODBC, which makes it less preferred for database access by companies. If your web scraping results will be stored directly in your database, you may need to integrate another layer for this step.
  • Python is not known to be one of the fastest programming languages. Indeed, according to Benchmarksgame, it is way slower than Java. However, the speed of the program depends a lot on the code and requests to the website when it comes to web scraping, hence, your web scraping process can be equally lengthy regardless of the programming language you use.


Bright Data is an established web scraping solution that uses Python to serve their clients’ web scraping needs.

Bright Data’s guide in Python for web scraping explains the technical details of a business use case.
Source: Bright Data

3- Ruby:

Compared to Python and JavaScript, Ruby is used less by programmers but it has specific features tailored for web scraping use cases. Ruby’s Nokogiri library offers powerful methods to parse HTML and XML, which are two common formats for web scraping outputs. 


  • Ruby’s syntax is not as interpretable as Python’s, but same functionality can be programmed with less lines of codes in Ruby
  • The Ruby Bundler makes it easier to manage and deploy packages from GitHub, which saves time especially for projects that need to use an existing package
  • Nokogiri library can deal with broken HTML code easier than other languages.


  • Given that Ruby is not as preferred as Python and Javascript, there are less resources and community tools available for Ruby.
  • Machine learning and NLP toolkits, which are popular use cases built on web scraping data, are not as developed for Ruby as it is for other languages like Python. If Ruby is preferred for data collection and parsing, the models may still need to be developed in other languages.

Alternative Solution: Readily Available Tools for Web Scraping

There are open source web scraping tools that are free to use. Some of them require no coding at all, but some of them will require a certain amount of code modification by the user. Most of these solutions are limited to scraping the page that the user is on and can not be scaled to scraping thousands of web pages in an automated manner.

For more information about the types of web scraping tools and a list of tools from 2023, check out web scraping tools: data-driven benchmarking.


  • Free of charge and often has a helpful community to get guidance from.
  • A quick solution to run a pilot if the business value of the web scraping needs to be tested.


  • Depends on a programming language or pre-built code, which takes time to understand and adapt.
  • Not all open source tools support or provide proxies and dynamic IP solutions which you may need to separately integrate in order not to be blocked by the websites you are trying to scrape.

Another option to use readily available tools is working with external web scrapers. These services can provide proxy services for scrapers or directly scrape the data and deliver it in the required format by the business. This allows technical teams to allocate time from data pulling to other development priorities. Especially for companies that do not have data engineers or developers to support data analytics teams, having readily available data saves significant time and effort.


  • Saves time for data pulling and parsing.
  • Often provides services in the cloud which makes the process faster and storing data easier. Learn more about the benefits of cloud web scraping from our detailed guide about the subject.
  • Handles dynamic IP and proxy issues without additional development needed by businesses.


For more on web scraping:

To explore more technical aspects of web scraping and its business use cases, read our articles:

For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us:

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This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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