The widespread adoption of sensor-enabled and mobile ubiquitous devices has caused an avalanche of big data that is mostly geospatially tagged. Most cloud-based big data processing systems are designed for general-purpose workloads, neglecting spatial-characteristics. However, interesting analytics often seek answers for proximity-alike queries. We fill this gap by providing custom geospatial service layer atop of Apache Spark. To be more specific, we leverage Spark to design a custom spatial-aware partitioning method to boost geospatial query performances. Our results show that our patches outperform state-of-the-art implementations by significant fractions.