During the rise and innovation of “big data,” the geospatial analytics landscape has grown and evolved. We are beyond just analyzing static maps. Geospatial data is streaming from devices, sensors, infrastructure systems, or social media, and our applications and use cases must dynamically scale to meet the increased demands.
Cloud can provide cost-effective storage and that ephemeral resource-burst needed for fast processing and low latency, all to monetize the immediate value of fresh geospatial data. Geospatial analytics require optimized spatial data types and algorithms to distill data to knowledge. Such processing, especially with strict latency requirements, has always been a challenge.
We propose an open source big data stack for geospatial analytics on Cloud based on Apache NiFi, Apache Spark and LocationTech GeoMesa. GeoMesa is a geospatial framework deployed in a modern big data platform that provides a scalable and low latency solution for indexing volumes of historical data and generating live views and streaming geospatial analytics.