Hagedorn, StefanGötze, PhilippSattler, Kai-UweMitschang, BernhardNicklas, DanielaLeymann, FrankSchöning, HaraldHerschel, MelanieTeubner, JensHärder, TheoKopp, OliverWieland, Matthias2017-06-202017-06-202017978-3-88579-659-6Big Data sets can contain all types of information: from server log files to tracking information of mobile users with their location at a point in time. Apache Spark has been widely accepted for Big Data analytics because of its very fast processing model. However, Spark has no native support for spatial or spatio-temporal data. Spatial filters or joins using, e.g., a contains predicate are not supported and would have to be implemented ine ciently by the users. Also, Spark cannot make use of, e.g., spatial distribution for optimal partitioning. Here we present our STARK framework that adds spatio-temporal support to Spark. It includes spatial partitioners, different modes for indexing, as well as filter, join, and clustering operators. In contrast to existing solutions, STARK integrates seamlessly into any (Scala) Spark program and provides more flexible and comprehensive operators. Furthermore, our experimental evaluation shows that our implementation outperforms existing solutions.enText/Conference Paper1617-5468