Amazon’s Simple Storage Service (S3) Amazon Web Service’s cloud storage service Capable of storing practically unlimited amounts of data Data is stored in directories called buckets Stored input and output data DynamoDB AWS’s fully-managed No-SQL database service DynamoDB was utilized to store geohash indexes and metadata for output imagery PostGIS Spatial database extender for PostgreSQL Most powerful open source spatial database engine Used to store output composite Landsat imagery and metadata.Extract Attribute Values from GeoTIFF Filename Select Only 1 Text File Per Scene and Read inģ. Of development and production environmentsĢ. Design / Test / Analyze various configurations.Explore process / issues/ requirements of.Spatial integration firm based in Lakewood, CO.Performance and Application of GIS and Big Data ETL Processes Using FME The successfulness of each case study was determined through analyzing a common set of success criteria, which includes: Total Processing Time, Ease of Setup, Cost, Ease of Viewing and Querying Results, Local System Requirements, and Network Requirements. Through a series of case studies, three main variables were examined: (1) location of and method of accessing source data, (2) location of system processing – locally verses in the cloud, and (3) file format and target destination of the output data. The control process was developed in FME Workbench that reads and processes single-band Landsat imagery and metadata text files and outputs composite images using 11 common band combinations. This was executed by designing, testing, and analyzing various configurations of development and production environments, ranging from entirely on-premises to entirely in the cloud. The purpose of the project was to expose the advantages and disadvantages of using local resources, cloud technology, and Big Data Software to view, process, and manipulate large amounts of geospatial data. This presentation will describe the process and results of a project, which was conducted as a graduate-level capstone project for Johns Hopkins University, that utilized both FME Desktop and FME Cloud to manipulate approximately one terabyte of Landsat imagery.

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