In recent years, the popularity of the Python programming language has been increasing. Not surprising when you consider that it is an ideal language for when you are dealing with Big Data.

Popular for a reason

In 2020 Python was still in 3rd place of most popular programming languages according to the Tiobe Index, but since the end of 2021 Python has been at the top of that same list. This increase in popularity is easy to explain if you know what Python excels at: analyzing Big Data and Machine learning.

The power of Python

A script in Python is on average three times as compact as the same script in another language. This compactness offers advantages on various levels. It's relatively easy for us as developers to develop an application in a shorter time. This also makes collaboration a lot easier; Python is very well-arranged and often shows you at a glance where someone else has left off. But this compactness also offers advantages for the user; even if an application has to fetch enormous amounts of data, with Python such an application continues to respond well.

These companies are reaping the benefits of Python

Excellence in Big Data, Machine Learning & Artificial Intelligence

We are all generating more and more data. Python is extremely suitable for processing all these large amounts of data and analyzing it. For example, Disney and Sony Dreamworks use Python to render their animations and movies. Spotify uses Python for the backend and data analysis of their users. Uber uses Python to predict waiting times, and Instagram's entire backend runs on Python as well. Even companies without millions of users see the advantages of a programming language that has no problem with data processing. For example, Python is used by NASA to process a lot of their data.

The all-rounder we've been dreaming of?

Are we going to use Python for everything from now on? Although it is a beautiful language because of the advantages mentioned above, the exact same advantages also give less desirable aspects. For example, the compactness is ideal for developing applications quickly, but due to the lack of things like Typesafe (which a language like TypeScript does offer), you will have to write more automatic tests to guarantee the stability of the code. This creates extra work, especially for large applications.


  • Python is relatively easy to learn, even for less experienced developers.
  • Compatible with services such as AWS.
  • Huge library with ready-made code.


  • Python is really very suitable for processing data. If you want something other than that, other languages are probably better suited.
  • Python can be less suitable when you prefer memory optimization.
Ready to work with Big Data and Python?

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