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We present a scalable, cloud-based science platform solution designed toenable next-to-the-data analyses of terabyte-scale astronomical tabulardatasets. The presented platform is built on Amazon Web Services (overKubernetes and S3 abstraction layers), utilizes Apache Spark and the AstronomyeXtensions for Spark for parallel data analysis and manipulation, and providesthe familiar JupyterHub web-accessible front-end for user access. We outlinethe architecture of the analysis platform, provide implementation details,rationale for (and against) technology choices, verify scalability throughstrong and weak scaling tests, and demonstrate usability through an examplescience analysis of data from the Zwicky Transient Facility's 1Bn+ light-curvecatalog. Furthermore, we show how this system enables an end-user toiteratively build analyses (in Python) that transparently scale processing withno need for end-user interaction.  The system is designed to be deployable by astronomers with moderate cloudengineering knowledge, or (ideally) IT groups. Over the past three years, ithas been utilized to build science platforms for the DiRAC Institute, the ZTFpartnership, the LSST Solar System Science Collaboration, the LSSTInterdisciplinary Network for Collaboration and Computing, as well as fornumerous short-term events (with over 100 simultaneous users). A live demoinstance, the deployment scripts, source code, and cost calculators areaccessible at http://hub.astronomycommons.org/.

The Astronomy Commons Platform: A Deployable Cloud-Based Analysis Platform for Astronomy