Recently, the volume of available spatial data increased tremendously. For instance, in November 2013 NASA announced the release of hundreds of Terabytes of its earth remote sensing dataset. Such data includes but not limited to: weather maps, socioeconomic data, vegetation indices, geological maps, and more. Making sense of such spatial data will be beneficial for several applications that may transform science and society - For example: (1) Space Science: that allows astronomers to study and probably discover new features of both the earth and the outer space, (2) Socio-Economic Analysis: that includes for example climate change analysis, study of deforestation, population migration, and variation in sea levels, (3) Urban Planning: assisting government in city planning, road network design, and transportation engineering, (4) Disaster Planning: that helps in assessing the impact of natural disasters. The main aim of this paper is to investigate novel data management techniques that enable interactive and scalable exploration of big spatial data. The paper envisions novel system architectures that provide support for interactive and spatial data exploration, as follows: (1) The paper suggests extending data analytics frameworks, e.g., Apache Spark, to support spatial data types and operations at scale. The resulting framework will serve as a scalable backbone for processing spatial data exploration tasks. (2) It also sketches novel structures and algorithms that leverage modern hardware, e.g., SSDs, and in-memory data processing techniques to efficiently store and access spatial data. Second, the paper proposes extending spatial database systems to support an exploration-aware spatial query evaluation paradigm through three novel components: (1) Spatial Query Steering: that allows the user to slightly modify the query conditions online (zooming in/out) and retrieve the new results in very low latency. (2) Recommendation-Aware Spatial Querying: that injects the recommendation functionality inside classical spatial query executors to support spatial data recommendation. It leverages recommendation algorithms to predict what spatial objects/areas the user would like based on her past interactions with the system. (3) Spatial Query Approximation: That aims at achieving interactive performance by studying the tradeoff between approximate spatial data exploration and query response time.