Spatial extract, transform, load (spatial ETL), also known as geospatial transformation and load (GTL), is a process for managing and manipulating geospatial data, for example map data. It is a type of extract, transform, load (ETL) process, with software tools and libraries specialised for geographical information. 
A common use of spatial ETL is to convert geographical information from a data source into another format that can be more easily used, for example by importing it into GIS software. A tool may translate data directly from one format to another, or via an intermediate format. Intermediate formats are often used when data transformation must be carried out.
Origins and history.
Although ETL tools for processing non-spatial data have existed for some time, ETL tools that can manage the unique characteristics of spatial data only emerged in the early 1990s.
Spatial ETL tools emerged in the GIS industry to enable interoperability (or the exchange of information) between the industry's diverse array of mapping applications and associated proprietary formats. However, spatial ETL tools are also becoming increasingly important in the realm of management information systems as a tool to help organizations integrate spatial data with their existing non-spatial databases, and also to leverage their spatial data assets to develop more competitive business strategies.
Traditionally, GIS applications have had the ability to read or import a limited number of spatial data formats, but with few specialist ETL transformation tools; the concept being to import data then carry out step-by-step transformation or analysis within the GIS application itself. Conversely, spatial ETL does not require the user to import or view the data, and generally carries out its tasks in a single predefined process.
With the push to achieve greater interoperability within the GIS industry, many existing GIS applications are now incorporating spatial ETL tools within their products; the ArcGIS Data Interoperability Extension being an example of this.
Transformation.
The transformation phase of a spatial ETL process allows a variety of functions; some of these are similar to standard ETL, but some are unique to spatial data. Spatial data commonly consists of a geographic element and related attribute data; therefore spatial ETL transformations are often described as being either "geometric transformations" – transformation of the geographic element – or "attribute transformations" – transformations of the related attribute data.
Additional features.
Desirable features of a spatial ETL application are:
Uses.
Spatial ETL has a number of distinct uses:
References.
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