About the Data Interoperability extension tutorial
The Data Interoperability extension tutorial introduces you to the tools and functionality that are available in ArcGIS when the extension is enabled. It is divided into three sections that guide you through direct-read formats and interoperability connections, quick conversion tools, and the fundamentals of transforming data using FME Workbench.
The exercises within each section demonstrate concepts and methods sequentially and therefore should be completed in the order they are presented. They are designed to allow you to work at your own pace without additional assistance using ESRI ArcTutor sample data.
You will need about 30–40 minutes of focused time to complete all the exercises.
This tutorial assumes you have installed the ESRI ArcTutor data at C:\arcgis\ArcTutor\Data Interoperability. If not, make the appropriate path changes throughout this tutorial. Ask your system administrator for the correct path to the tutorial data if you do not find it at the default installation path.
Using direct-read formats and connections
Translating data with the quick conversion tools
- In Exercise 2a: Importing data with the Quick Import tool, you import Geography Markup Language (GML) zoning data using the Quick Import tool.
- In Exercise 2b: Exporting data with the Quick Export tool, you export geodatabase zoning data to a MapInfo TAB dataset using the Quick Export tool.
- In Exercise 2c: Automating quick conversion tools with ModelBuilder, you create a model that imports GML zoning data, aggregates features based on specific attributes, and exports the results to GML and MapInfo TAB datasets.
Transforming data with spatial ETL tools
- In Exercise 3a: Getting started with spatial ETL, you create a simple spatial ETL tool that reads Intergraph Modular GIS Environment (MGE) parcel data and loads it into a geodatabase.
- In Exercise 3b: Transforming data and using Visualizer, you learn the fundamentals of using FME transformers by adding them to the spatial ETL tool you created in "Getting started with spatial ETL."
- In Exercise 3c: Using source attributes to separate data, you learn how to use spatial ETL to categorize data and generate sets of features with common values.