Extraction, Transformation, and Loading (ETL) Module for Hotspot Spatial Data Warehouse Using Geokettle

Spatial data warehouse technology is one solution to the problem of big spatial data. Accumulation In the process of making spatial data warehouse, extraction, transformation, and loading (ETL) process has an important role to determine the quality of data. Manual ETL process requires a long time and makes a lot of queries. Therefore, this research uses Geokettle as a spatial ETL tool to integrate spatial data. This research used hotspot dataset of Indonesia from 2006 to 2014 and administrative districts data in Indonesia. This research performed ETL modeling with the simplification, adjustment, and design of ETL scenarios. The result of this research is ETL modeling implemented using Geokettle. SpagoBI Studio was used to create multidimensional data cubes. Moreover ETL testing was conducted using Geokettle, and spatial data warehouse testing was done by comparing the total number of hotspots between SQL query result and spatial analysis hotspot result on Quantum GIS.