The talk presents the details of designing and developing Apache Sedona, which extends the core engine of Spark and SparkSQL to support spatial data types, indexes, and geometrical operations at scale. The talk also gives a detailed analysis of the technical challenges and opportunities of extending Spark to support state-of-the-art spatial data partitioning techniques: uniform grid, R-tree, Quad-Tree, and KDB-Tree. The talk will explain how building local spatial indexes, e.g., R-Tree or Quad-Tree, on each Spark data partition can speed up the local computation and hence decrease the overall runtime of the spatial analytics program. Extensive experiments on real spatial datasets show that Sedona achieves up to two orders of magnitude faster run time performance than existing Hadoop-based systems and up to an order of magnitude faster performance than Spark-based systems.