Table of Contents

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  1. Preface
  2. Using the Designer
  3. Working with Sources
  4. Working with Flat Files
  5. Working with Targets
  6. Mappings
  7. Mapplets
  8. Mapping Parameters and Variables
  9. Working with User-Defined Functions
  10. Using the Debugger
  11. Viewing Data Lineage
  12. Comparing Objects
  13. Managing Business Components
  14. Creating Cubes and Dimensions
  15. Using the Mapping Wizards
  16. Datatype Reference
  17. Configure the Web Browser

Understanding the Mapping

Understanding the Mapping

The Type 3 Dimension mapping performs the following tasks:
  • Selects all rows.
  • Caches the existing target as a lookup table.
  • Compares logical key columns in the source against corresponding columns in the target lookup table.
  • Compares source columns against corresponding target columns if key columns match.
  • Flags new rows and changed rows.
  • Creates two data flows: one for new rows, one for updating changed rows.
  • Generates a primary key and optionally notes the effective date for new rows.
  • Inserts new rows to the target.
  • Writes previous values for each changed row into
    previous
    columns and replaces previous values with updated values.
  • Optionally uses the system date to note the effective date for inserted and updated values.
  • Updates changed rows in the target.
The following figure shows a mapping that the Type 3 Dimension option in the Slowly Changing Dimensions Wizard creates:
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The Type 3 Dimension mapping uses a Lookup and an Expression transformation to compare source data against existing target data. When you step through the Slowly Changing Dimensions Wizard, you enter the lookup conditions (source key columns) and source columns that you want the Integration Service to compare against the existing target. The Designer creates additional columns for the change columns to hold historic data.
For each source row without a matching primary key in the target, the Expression transformation marks the row new. For each source row with a matching primary key in the target, the Expression compares user-defined source and target columns. If those columns do not match, the Expression marks the row changed. The mapping then splits into two data flows.
The first data flow uses the Filter transformation, FIL_InsertNewRecord, to filter out rows. The Filter transformation passes only new rows to the UPD_ForceInserts Update Strategy transformation. UPD_ForceInserts inserts new rows to the target. A Sequence Generator creates a primary key for each row. If you select the Effective Date option in the mapping wizard, the Designer creates an Expression transformation, EXP_EffectiveDate_InsertNew. The Integration Service uses the system date to indicate when it creates a new row.
In the second data flow, the FIL_UpdateChangedRecord Filter transformation allows only changed rows to pass to the Update Strategy transformation UPD_ChangedInserts. In addition, the Filter transformation updates the changed row: it takes the new versions of data from the source qualifier, and uses existing versions of dimension data (passed from the Lookup transformation) to populate the
previous
column fields. UPD_ChangedInserts inserts changed rows to the target. If you select the Effective Date option in the mapping wizard, the Designer creates an Expression transformation, EXP_EffectiveDate_InsertChanged. The Integration Service uses the system date to indicate when it updates a row.


Updated July 02, 2020