Successful companies use analytic measures to identify and reward their best projects and contributors. Successful open source developers often make similar decisions when they evaluate whether or not to reward a project or community by investing their time. This talk will show how Spark enables a data-driven understanding of the dynamics of open source communities, using operational data from the Fedora Project as an example. With thousands of contributors and millions of users, Fedora is one of the world’s largest open-source communities. Notably, Fedora also has completely open infrastructure: every event related to the project’s daily operation is logged to a public messaging bus, and historical event data are available in bulk. We’ll demonstrate best practices for using Spark SQL to ingest bulk data with rich, nested structure, using ML pipelines to make sense of software community data, and keeping insights current by processing streaming updates.
William Benton leads a team of data scientists and engineers at Red Hat, where he has applied analytic techniques to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His current focus is designing infrastructure for next-generation data-driven applications, but he has also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology. Benton holds a PhD in computer sciences from the University of Wisconsin.