Tag Archives: slowly changing

Having worked a lot with Analysis Services Multidimensional Model in the past it has always been a pain when building models on facts and dimensions that are only valid for a given time-range e.g. following a Slowly Changing Dimension Type 2 historization. Opposed to relational engines, Analysis Services Multidimensional Model does not support complex relationships that would create more than one matching row like in terms of SCD2 a join using T-SQL’s BETWEEN. In Multidimensional Models each fact-row has to be linked to exactly one member in a dimension. There are some techniques to mimic BETWEEN-Joins like abusing ManyToMany-Relationships but all of them have several drawbacks and are just workarounds that do not solve the actual problem.

Fortunately PowerPivot (and in the future Analysis Services Tabular Models that are part of SQL Server 2012) offer more flexibility on modeling data and relationships that we can leverage to solve such scenarios.

In this post I will cover a warehouse-scenario where articles are stored in warehouses for a given period of time. The model should allow analysis of the current stock on a daily base. All tables follow a SCD2 historization and shall always display the currently valid records for the selected time.

Our model consists of 4 tables:

Article:

Warehouse:

Date:

Contains Date-Values from 2012-01-01 till 2012-01-15

Inventory (Facts):

As you can see our facts are related using the BusinessKey (BK) and are thereby independent of any historical changes within any dimension.

This means that SCD2-changes in dimension have no impact on our fact-table reducing most of the complex ETL that is usually required to propagate those changes to the fact-table to allow in-time analysis

Once you have loaded these tables into PowerPivot and try to create relationships you will get errors as none of the tables can be related as the BKs are not unique in any table. But that’s OK as all tables have to be filtered on the ReferenceDate first to show the structure at selected date.

So, how can we find out which rows are currently active?

First of all we have to create a measure that returns the LastDate in the current time-slice (assuming that we always want to get the most current structures, e.g. 31st of December when doing analysis over a whole year etc.)

Using LASTDATE()-function this can be accomplished very easily:

ReferenceDate:=LASTDATE('Date'[Date]) 

All other tables have to be filtered on this ReferenceDate to get only the valid rows at this point in time.

I used a combination of COUNTROWS() and FILTER() to create a measure that identifies a value as valid or not:

For our Article-Table:

Article_IsValid:=COUNTROWS(
FILTER(
    Article;
    (Article[VALID_FROM] <= 'Date'[ReferenceDate]
        || ISBLANK(Article[VALID_FROM]))
    && (Article[VALID_TO] > 'Date'[ReferenceDate]
        || ISBLANK(Article[VALID_TO]))))>0

For our Warehouse-Table:

Warehouse_IsValid:=COUNTROWS( 
FILTER(
    Warehouse;
    (Warehouse[VALID_FROM] <= 'Date'[ReferenceDate]
        || ISBLANK(Warehouse[VALID_FROM]))
    && (Warehouse[VALID_TO] > 'Date'[ReferenceDate]
        || ISBLANK(Warehouse[VALID_TO]))))>0

And also for our Fact-Table:

Inventory_IsValid:=COUNTROWS( 
FILTER(
    Inventory;
    (Inventory[VALID_FROM] <= 'Date'[ReferenceDate]
        || ISBLANK(Inventory[VALID_FROM]))
    && (Inventory[VALID_TO] > 'Date'[ReferenceDate]
        || ISBLANK(Inventory[VALID_TO]))))>0

Using a pivot-table we can see the WarehouseOwner changing over time. The owner of WH2 changes to John with 2012-01-04, as Dave does not own any warehouse by that time anymore, his entry also gets invalidated:

To get the valid Amount at a given ReferenceDate we have to sum only the valid rows for that date:

Amount:=CALCULATE( 
SUM( 
      [Amount_BASE]); 
      FILTER(Inventory; Inventory[Inventory_IsValid]))

This calculation is not linked to any other table at the moment but this can be achieved by adding further criterias to the FILTER-function.

Using CONTAINS()-function we can simulate relationships based on the BusinessKeys:

Amount:=CALCULATE( 
SUM(
    [Amount_BASE]); 
    FILTER(
        Inventory;
        Inventory[Inventory_IsValid] 
        && CONTAINS(
            FILTER(VALUES(Article[Article_BK]); Article[Article_IsValid]);
            Article[Article_BK];
            Inventory[Article_BK]) 
        && CONTAINS(
            FILTER(VALUES(Warehouse[Warehouse_BK]);Warehouse[Warehouse_IsValid]);
            Warehouse[Warehouse_BK];
            Inventory[Warehouse_BK])))

For each table that we want to relate we have to add another CONTAINS()-function to our calculation to mimic a relationship.

Taking the relationship to table ‘Warehouse’ as an example:

VALUES(Warehouse[Warehouse_BK]) returns only rows/BKs within the current context of table Warehouse.

Those rows are further filtered by the ReferenceDate using our [Warehouse_IsValid]-Measure returning only rows that are valid for the selected ReferenceDate.

The two other parameters of the CONTAINS()-function define the columns that are matched.

Doing this for all tables allows us to analyze our current stock-level with the current structures at any given point in time:

As empty rows are suppressed from pivot-tables by default, we get the desired result when filtered on a specific date:

I have not tested his approach yet in terms of performance when it comes to bigger volumes of data, this will be part of an upcoming post.

Further I will show how this approach can be extended to support snowflaked schemas as they are very often found in data warehouses.

Part2: Handling SCD2 snowflake-schemas with PowerPivot