Spatial ETL tools provide the data processing functionality of traditional Extract, Transform, Load (ETL) software, but with a primary focus on the ability to manage spatial data (which may also be called geographic, map or location data).
Extract and Load
Spatial data, more than any other, suffers from the problems of data held in different formats (whether proprietary or open) and adhering to different standards. Therefore a key requirement for a Spatial ETL system is that it be capable of handling as many data formats as possible, in a consistent manner.
The conversion of spatial data between the source (extract) and destination (load) formats is often referred to as spatial data translation. A spatial ETL system may translate data directly from one format to another, or via an intermediate format; the latter being more common when transformation of the data is to be carried out.
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.
Common Geometric Transformations
- Reprojection: the ability to convert spatial data between one coordinate system and another.
- Spatial transformations: the ability to model spatial interactions and calculate spatial predicates
- Topological transformations: the ability to create topological relationships between disparate datasets
- Resymbolisation: the ability to changes the cartographic characteristics of a feature, such as colour or line-style
- Geocoding: the ability to convert attributes of tabular data into spatial data
Desirable features of a spatial ETL application are:
- Data Comparison: Ability to carry out change detection and do incremental updates
- Conflict Management: Ability to manage conflicts between multiple users of the same data
- Data Dissemination: Ability to publish data via the internet or deliver by email regardless of source format
- Semantic Processing: Ability to understand the rules of different data formats to minimize user input whilst preserving meaning
Spatial ETL Uses
Spatial ETL has a number of distinct uses to which it is put.
- Data cleanup: The removal of errors within a dataset
- Data Merging: The bringing together of multiple datasets into a common framework - Conflation is a good example of this
- Data verification: The comparison of multiple datasets for verification and quality assurance purposes
- Data translation: The conversion of spatial data from one format to another with no intended change in structure or schema
Spatial ETL - 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.
Spatial ETL and GIS
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 a good example of this.
Spatial ETL and ETL
Mindful of the degree to which any data can be assigned a fixed geographic position, and of the proliferation of spatial capabilities within standard relational or object databases, vendors of standard ETL applications are attempting to incorporate Spatial ETL functionality within their products.
Spatial ETL tools
- Snowflake Software Ltd Solutions for standards based data exchange.
- Safe Software Inc.
- PCI Geomatics Enterprises Inc.
- WisdomForce Technologies supplies software for Oracle Spatial.
- SpatialDataIntegrator is an open source software powered by Talend and developed by camptocamp.
- GeoKettle is another Open Source Spatial ETL tool.
- Business intelligence
- Object-relational database