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. POWERCENTERHELP
  17. Datatype Reference
  18. Configure the Web Browser

Understanding the Mapping

Understanding the Mapping

The Type 2 Dimension/Effective Date Range 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 three data flows: one for new rows, one for changed rows, one for updating existing rows.
  • Generates a primary key and beginning of the effective date range for new rows.
  • Inserts new rows to the target.
  • Generates a primary key and beginning of the effective date range for changed rows.
  • Inserts changed rows in the target.
  • Updates existing versions of the changed rows in the target, generating the end of the effective date range to indicate the row is no longer current.
The following figure shows a mapping that the Type 2 Dimension/Effective Date Range option in the Slowly Changing Dimensions Wizard creates:
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The Type 2 Dimension/Effective Date Range 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.
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 three data flows.
The first data flow uses the Filter transformation, FIL_InsertNewRecord, to filter out existing 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. The Expression transformation, EXP_KeyProcessing_InsertNew, uses the system date to indicate the start of the effective date range. The transformation leaves the end date null, which indicates the new row contains current dimension data.
In the second data flow, the FIL_InsertChangedRecord Filter transformation allows only changed rows to pass to the Update Strategy transformation, UPD_ChangedInserts. UPD_ChangedInserts inserts changed rows to the target. The Expression transformation, EXP_KeyProcessing_InsertChanged, uses the system date to indicate the start of the effective date range. The transformation leaves the end date null, which indicates the changed row contains current dimension data.
In the third data flow, for each changed row written to the target, the Filter transformation, FIL_UpdateChangedRecord, passes the primary key of the previous version to the Update Strategy transformation, UPD_ChangedUpdate. UPD_ChangedUpdate updates rows in the target. The Expression transformation, EXP_UpdateChanged, updates the end date column with the system date. This changes the status of the dimension from the current version to a previous version.


Updated June 25, 2018