Take a look at any number of impressionist paintings. When seen from a distance, the thousands of colored brush strokes are layered to compose a bigger picture.
Like impressionism, massive data sets are unintelligible until they're analyzed in both a macro and micro view - making data analysis technologies, like data visualization, increasingly important.
However, one limitation for data analysis programming tools is the ability to collaborate with and share code amongst other developers. In creating our own data analysis software, researchers at AT&T Labs realized they couldn't easily share and implement code, since it was only deployable on the local machine in which it was developed. To solve this problem, they created RCloud, a collaborative platform built in the cloud that enables developers to see and deploy code that their peers have already created across coding languages.
Using "notebooks" stored in the cloud, others can easily repurpose code or tweak it to achieve their desired outcomes. These notebooks also enable people in non-technical roles to understand and analyze data.
Built on the R programming language - popular among data scientists - our researchers quickly realized that RCloud would be useful for other data scientists, which is why they've released RCloud software into GitHub's open source community.