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.
My latest book was released a few weeks ago. This project has been one of the most challenging projects I've ever done, and not necessarily for the reasons I would have originally thought. Here is a little story of those fifteen months writing the IPython cookbook.
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.