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With the increasing popularity of computational approaches to conduct social science research, building a scalable and efficient computing platform has become a topic of interest for academia to empower research labs and institutes to analyze large-scale data. While social science researchers have been very excited about the advancement of emerging technologies in big data, deep learning, computer vision, network analysis, etc., they are also constrained by the available computing resources to analyze data. This paper describes a scalable solution to deploy JupyterHub for computational social science research on the cloud. We use a reference architecture on AWS to walk through the design principles and details. Our architecture has helped facilitate several collaborations between Facebook and academia. The case study (Facebook Open Research and Transparency platform) shows that our architecture, using technologies like containerization and serverless computing, can support thousands of users to analyze web-scale datasets. |
