The following diagram demonstrates the transformation process a spreadsheet goes through when processed by DataDefractor:

1. A sample set of similar spreadsheets is passed to DataDefractor Mapping Wizard.
2. The user steps through the wizard to develop a spreadsheet mapping schema following these simple steps:
   • Break the input spreadsheets into simpler virtual spreadsheets.
   • Define the dimensional model captured in the sample spreadsheets.
   • Map the virtual spreadsheets to the dimensional model.
   Once developed, the mapping schema is saved.
3. Using the saved mapping schema, DataDefractor Normalization Processing is invoked and executed repeatedly against any number of input spreadsheets. DataDefractor automatically dissects the spreadhseets, extracts the fact data and context metadata and outputs a fully populated dimensional model.

The physical format of the output depends on the edition of DataDefractor:

• DataDefractor Solo outputs a star schema in the form of multiple CSV files. Alternatively it outputs the entire data model into a single flattened recordset CSV file.
• DataDefractor SSIS outputs a star schema directly into the SQL Server Integration Services data flow pipeline.

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