High Performance and Scalable Geospatial Analytics on Cloud with Open Source

High Performance and Scalable Geospatial Analytics on Cloud with Open Source

Wednesday, June 20
4:00 PM - 4:40 PM
Meeting Room 211A/B/C/D

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.

SPEAKERS

Constantin Stanca
Solutions Engineer
Hortonworks
Constantin is a top Hortonworks Community Connection contributor publishing multiple articles around stream analytics and geospatial. Through his work at Hortonworks, leveraging his vast experience over the last two decades building large-scale data processing systems using a variety of database technologies, he works to build end-to-end solutions involving big data technologies. He holds a Ph.D. in numerical modeling and computer simulation in the field of petroleum engineering. He is also a certified data scientist, PMP and ScrumMaster.
James Hughes
Mathematician
CCRi
Dr. James Hughes is a mathematician at Commonwealth Computer Research, Inc. in Charlottesville, Virginia. He is a core committer for GeoMesa which leverages Accumulo, HBase and other distributed database systems to provide distributed computation and query engines. He is a LocationTech committer for GeoMesa, SFCurve, and JTS. He serves on the LocationTech Project Management Committee and Steering Committee. Through work with LocationTech and OSGeo projects like GeoTools and GeoServer, he works to build end-to-end solutions for big spatio-temporal problems. He holds a PhD in algebraic topology from the University of Virginia.