Turn fragmented geospatial and enterprise data into trusted, reusable, decision-ready information through visual spatial ETL
Trusted by Enterprises Worldwide Since 2012
Less manual processing time
Faster recurring delivery
Sources and formats consolidated
More valid or complete records
GIS FILES, DATABASES AND SERVICES IN ONE FLOW
GEOMETRY TREATED AS A FIRST CLASS DATA TYPE
POWERED BY GEOAI AND AI/ML
REUSABLE TRANSFORMATIONS, ORCHESTRATED JOBS
Spatial and non-spatial data move through one governed workflow, so disconnected processing becomes repeatable data operations.
Visual spatial
ETL
Design end-to-end data pipelines in a graphical, metadata-driven environment. Configurable steps make workflow logic easier to build, inspect, reuse and maintain.
Geospatial data interoperability
Read and write supported GIS formats and work with GML, KML, OGR-based sources, spatial databases and service interfaces including WFS, SOS, CSW and WPS.
Data transformation and quality
Filter, join, validate, standardize, restructure, enrich and route data. Apply consistent rules before information reaches maps, applications, dashboards or systems of record.
Spatial
processing
Treat geometry as a first class data type. Transform coordinate reference systems, assign spatial references, apply geometry-aware analysis and topological processing inside the data flow.
Workflow automation and delivery
Build repeatable transformations and multi-step jobs with preview, execution logging and reusable logic. Deliver results to operational databases, analytical stores, files and services.
Extensible
analytics
Extend workflows with JavaScript-based processing, 2D and 3D geometry operations, point projection steps and an AI or ML integration path that includes linear regression.
NetMapper brings them into one flow, with geometry treated as a first class data type.
Request a NetMapper Demo
Repeatable process instead of repetitive file handling. Recurring spatial work becomes one defined, reusable flow.
Connect data
GIS files, databases, structured data, enterprise sources and standards-based geospatial services.
Improve quality
Clean, validate, standardize, restructure and enrich data before it reaches operational systems.
Automate workflows
Recurring conversions become reusable visual transformations and orchestrated jobs.
Unify spatial context
Business attributes and geometry processed together, including coordinate reference systems.
Deliver trusted outputs
Publish consistent data to spatial databases, warehouses, GIS files and web services.
Data modernization
and migration
Move legacy files and databases into modern spatial plattorms, transforming schemas and preserving geometry.
Enterprise GIS integration
Connect GIS workflows with operational and analytical systems so location intelligence joins the enterprise data flow.
Spatial data warehouse preparation
Build consistent, location aware feeds for reporting, dashboards, OLAP and SOLAP analytics.
Infrastructure and asset data integration
Consolidate asset, network, land, transport, utility and environmental data from multiple departments.
Infrastructure and asset data integration
Automate preparation and delivery of frequently updated datasets to files, databases or services.
Data quality and standardization
Apply repeatable validation and transformation rules before data is published or loaded.
What teams get out of it
Less manual effort across recurring data conversions and publishing cycles.
More consistent processing through reusable, documented workflows.
Faster preparation of spatial data for GIS, analytics and applications.
Better alignment between GIS teams and enterprise data teams.
Improved confidence through validation and standardization before delivery.
Specialist capacity spent on analysis rather than repetitive file handling.
Five stages from a blank visual designer to a job your team runs on every data refresh.
STAGE 1
Design the flow
Select source connections and arrange processing steps in the visual designer.
STAGE 2
Configure the rules
Define joins, filters, validations, spatial operations, coordinate transformations and target mappings.
STAGE 3
Test and inspect
Preview results, review execution logs and resolve exceptions before operational use.
STAGE 4
Run and reuse
Execute the workflow as a repeatable transformation or an orchestrated job.
STAGE 5
Deliver trusted data
Publish outputs to the file, database, warehouse or service downstream teams need.
Data engineering and integration teams
GIS managers and geospatial specialists
Database and data warehouse teams
Bl and analytics leaders
System integrators and consultants
Government, utility, transport, telecom & infrastructure
Direct answers to what comes up most in early conversations.
Spatial ETL extracts location based and business data from source systems, transforms it, including geometry and coordinate reference system operations, and loads it into a target file, database, warehouse or service.
Create a reusable foundation for geospatial integration, data quality, automation, analytics and decision support.
