Big geospatial data is an emerging sub-area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges to these and other cognate disciplines. First, raster and vector data structures and analyses have developed on largely separate paths for the last twenty years and this creates an impediment to researchers utilizing big data platforms that do not promote the integration for these classes. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio-temporally analyze big geospatial datasets. IPUMS-Terra, a National Science Foundation cyberInfrastructure project, begins to address these challenges. IPUMS-Terra is a spatial data infrastructure project that provides a unified framework for accessing, analyzing, and transforming big heterogeneous spatio-temporal data, and is part of the IPUMS (Integrated Public Use Microdata Series) data infrastructure. It supports big geospatial data analysis and provides integrated big geospatial services to its users. As IPUMS-Terra's data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses.