Support of high performance queries and analytics on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems and emerging scientific applications such as pathology imaging. There are two major challenges for managing and querying massive spatial data: the explosion of spatial data, and the high computational complexity of spatial queries due to its multi-dimensional nature. Our goal is to develop a general framework to support high performance spatial queries and analytics for spatial big data on MapReduce and CPU-GPU hybrid platforms. In this talk, I will present a scalable and high performance spatial data warehousing system Hadoop-GIS for running large scale spatial queries on Hadoop and Spark. Hadoop-GIS achieves scalable and efficient queries through optimized spatial partitioning, multi-level indexing, customizable spatial query engine RESQUE and implicit parallel spatial query execution. I will introduce applications of the system to support pathology imaging analytics and social media analytics.