In this lecture, the students learn how current systems for dealing with big data are organized, implemented, programmed, deployed, and used. We focus on fundamental mechanisms including data distribution, data replication, distributed programming models, MPI and MapReduce. In addition, we discuss specifics of spatial data including different ways of distirbuting this data (R-trees, grids, space-filling curves) and when and how to use them.
By completing this module, students will be enabled to work with big datasets of spatial nature both in scientific environments using cluster computing architectures based on MPI as well as in scalable computing models such as cloud computing more tailored to business adoption.