There are many data visualization tools out there. Yet, I believe we're still lacking a robust, scalable, and cross-platform visualization toolkit that can handle today's massive datasets.
Most existing tools target simple plots with a few hundreds or thousands of points: bar plots, scatter plots, histograms and the like. Typically, these figures represent aggregated statistical quantities. Maps are also particularly popular, and there are now really great open source tools.
Perhaps contrary to a common belief, this is not the end of the story. There are much more complex visualization needs in academia and industry, and I've always been unsatisfied by the tools at our disposal.
In this post series, I'll talk about the big data visualization platform I'm currently developing with WebGL. I'll give in this first post the main motivations for this project. The next posts will contain the technical details.
I've been programming in
for a few months. Like a lot of programmers,
I learnt the language by myself, thanks to various tutorials, books or e-books
on the subject. One couldn't say there's a lack of resources on this
20-years old language since it's so widely used throughout the world. Yet,
I was surprised to discover a few weeks ago that the vast majority of what
I learnt has been obsolete for almost a decade. The reason is that too many
textbooks and tutorials on the Internet about OpenGL refer to a deprecated
way of programming and which relates to the fixed-function pipeline.
The modern way of programming in OpenGL is to use the programmable pipeline
through shaders. The
free e-book by
Jason McKesson is a very good
resource for learning modern OpenGL programming using the programmable