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Ruby and Python a powerful combination. What’s special about using them together?

Adam Manczuk

Ruby and Python are two highly sought-after programming languages. They share many similarities and both are powerful options that solve specific problems. But few people know that Ruby and Python can be brought together to create efficient apps with capabilities for heavy calculations and handling Big Data. In this article, we’ll take a look where each of these languages shines, and what’s special about using them together.

Similarities between Ruby and Python

Let’s start at the beginning: Python was born in 1991, and Ruby appeared four years later, in 1995. They are dynamically typed, multi-paradigm languages often chosen as new developer’s first language. They are comparatively easy to use and use pleasant syntax that makes writing code truly enjoyable. Object-oriented, functional and metaprogramming are all valid options with these two. For a while, both were used primarily in web apps - Ruby with its framework Rails, and Python with Django. Both of these are still excellent options, but the use of Python is shifting towards different major specialisations.

Python - heavy calculations favored in academia

The academic community has begun to appreciate Python for its flexibility and the freedom it allows. Python could even be called a framework for creating your own language. This is one of the reasons why the MIT has changed a decades-old tradition and shifted from teaching their introductory programming courses in LISP, to using Python. Only Python was able to rival LISP’s on the fly code modification - that is, changing the program while it is running.

Currently, Python is used by research teams as a tool that can integrate external modules and libraries responsible for handling large volumes of data. Meanwhile, on the web development front, Django remains an efficient framework for creating advanced web projects. Thanks to the REST API Celery, Python is an alternative to Java Spring - Django offers similar capabilities paired with ease of use, and a lower barrier to entry. Additionally, because Django is open source and fast, projects that use it cost about half as much as projects using .NET or Java.

The real game changer though? Pandas. This framework is the preferred basis for any Big Data project. For data analytics, Python brings in Jupyter Notebook. This open source web app supports live code, various equations, eye-catching visualizations, and regular text. As a result, Python is a great choice for projects that require Machine Learning, Computer Vision, and similar solutions.

Python is the most popular “glue” for a dozen specialized, scientific libraries. It allows for easy and interactive calculations of huge amounts of data, just by typing a command directly in a web application. Another use case is analyzing stock market prices in real time. For Artifact Intelligence, there are a dozen available options. Based on all this, would you guess that Python is the programming language used the most often at NASA? If so, you would guess correctly.

Are there any downsides to Python? Elasticity and freedom come with a higher risk of making mistakes. The responsibility rests more with the programmer, not the language or framework. With an experienced tech team, this ceases to be a problem.

Ruby - the best choice for rapid development

Ruby offers an approach to writing apps that many developers and product owners absolutely love: it never forces the project team to reinvent the wheel. Ruby comes with gems - libraries created by the community, each responsible for a feature or function. For example, if your development team needs to add the log-in functionality to your app (with user accounts, email verification, etc.), they don’t need to build it from scratch. Instead, they pick the appropriate gem and add it to the project.

This may sound like Ruby apps are build from modules - which is true, but only in part. Each gem can be modified as it’s introduced, allowing for a level of customization that’s more than satisfactory for modern web app development.

This approach has two important benefits:

  • Ruby allows for creating apps very quickly by using and adapting existing building blocks,
  • Because of its somewhat schematic approach (to development, testing, best practices, etc.) and a community push in the same direction, Ruby helps programmers develop very good habits as they work.

The first benefit means that Ruby is perfect for rapid prototyping and MVP creation. The second is an implied assurance of code quality - it’s simply much more difficult to write bad Ruby code.

Ruby on Rails - the language and the framework - are a very popular choice for developing web apps in the most cost-effective manner. If you’ve looked at how much it costs to cooperate with Ruby developers, this assertion may surprise you, but it’s true. How? Ruby projects are finished in a fraction of the time necessary with other languages and frameworks. So not only do you need to pay for a shorter cooperation, you can begin gaining traction and earning money more quickly.

Ruby is also a very mature framework, with a community that cares deeply about quality and best practices. Ruby on Rails is a solid, secure, reliable choice - even for enterprise solutions or fin-tech apps that handle sensitive data.

Using Ruby with Python - a powerful combination

If you want to build a web app that looks great, delights users, and handles Big Data or uses Machine Learning, joining the forces of Python and Ruby is a very good idea. How does it work?

At the top of the app structure, the layer users interact with, you’ll have JavaScript making the UI, animations, etc. beautiful and highly usable.

One step lower, Ruby on Rails will serve as the support structure with all of the app’s features. The things under the hood, like what needs to happen when a new user registers an account (communication with the database, for example).

Lower still, Python will run a sort of mining operation - it will connect your web app to powerful data processing and analytics tools. These are external libraries that Python just happens to be integrated with.

Finally, at the very bottom, these tools, like Jupyter or Pandas, will do the heavy lifting with your data.

Sounds abstract? Here’s an example.

Creditspring - a subscription-based loan system

This innovative solution allows users to pay a stable monthly fee for the ability to take out guaranteed loans. This way, they don’t need to worry about interest rates or paying the loan off. Users receive the loans on-demand, without having to go through an approval process, and there are no hidden costs.

The project required a number of competences, from UX design, through web app development with Ruby on Rails, to handling sensitive payment information with Python and Jupyter Notebook. The client was more than satisfied with the results.

If your project requires a vast array of skills and deep commercial experience, contact us.

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