»Scalable Geospatial Data Science with Python and Open Source Projects«
2020-09-21, 15:20–15:50, Room 2
The big data landscape is vast and continuously growing and it is also becoming aware of the challenges when dealing with geospatial data. Here I will go over and compare the recent open source developments in this space that enable working with geospatial data at scale.
This talk will cover an overview of the current open-source projects that enable and scale analytics and processing with geospatial vector data like points and polygons on a map. The main opponents will be the blue and yellow elephant in the room or more commonly known as PostgreSQL and Apache Hadoop. Python will be standing as a central language based on the recent projects and developments in the geospatial space.
The talk will further explore the other projects that are relevant in this space like Apache Spark, GeoMesa, Dask, ElasticSearch, MongoDB and other lesser known open source solutions that offer geospatial functionality. Other aspects that will be compared, will be spatial indexing, performance and personal experience and caveats along the way.