I was doing a Google search a couple of days ago and particularly, I wanted to know what are the options for developers who need to visualize large amounts of data points within applications. To my surprise, I couldn't really find that many solutions, and in fact, most of the results I got were from already many years ago.

But there was one result that grabbed my attention and it was a Reddit post about a user asking how to plot billions of data points? While other users replied saying "try using Power BI or Excel", other users suggested workarounds like using a Pandas DataFrame in the Python environment in order to store and manipulate data. But the user who posted the question didn't want to store and manipulate data, the user wanted to visualize the data!

So, how to visualize billions of data points and why would you do that? 

The fact that data and data visualization are everywhere in today's world, makes me wonder how data visualization impacts medical applications and what's their role within patients' diagnoses? Undoubtedly, patients trust their wellness on healthcare professionals' criteria and decisions but what do doctors, nurses, practitioners, and other healthcare professionals base their judgment and decisions on?

Money makes the world go round and as such, tracking and controlling financial transactions and information are some of the most important activities to do for any business, financial institution, or individual in today's connected world. And for that, you've got Fintech applications.

Depending on the purpose, Fintech applications may have different features that make them relevant for users. For example, stock market applications usually feature high-performance technical indicators to visualize and monitor measures such as price changes and market indexes while banking applications might allow private users to track their expenses and apply for credits without having to put a foot in the bank.

Large companies in the motorsports industry are collecting huge volumes of real-time data coming from different sources on and off the pitch during racing competitions.

These data are both collected from the car and the car engine itself and are meant to help drivers and engineers to understand the behavior of the machine in a dynamic environment.

Understanding the behavior of the vehicle during a race is key to making improvements in the performance of the entire racing team (such as Formula 1 teams!). High-performance data visualization in motorsports can lead a team to an improvement in performance during a race.

N-body simulation is maybe one of the most advanced data visualizations types out there. The truth is that we’re not talking anymore about visualizing traditional data with a business focus and it goes even beyond data from advanced sources such as vibration analysis.

The LiDAR method conveys another yet complex level of data visualization as it is widely used in demanding industries that require to plot, e.g., topographic or cartography 3D data.

Charting libraries are at a high peak and their development and usage are becoming even more popular in languages like JavaScript. As proof, there are a lot of JavaScript charting libraries out there with all sorts of capabilities to fulfill users’ needs, both commercial products, and open-source projects.