Handling Slowly Changing Dimensions in Analysis Services

Slowly Changing Dimensions (SCDs) are a fundamental concept in data warehousing and business intelligence. They represent attributes in a dimension that can change over time, but not frequently. Effectively managing these changes is crucial for maintaining historical accuracy and enabling robust analysis. This article explores common SCD types and their implementation within SQL Server Analysis Services (SSAS).

Understanding Slowly Changing Dimensions

A slowly changing dimension is an attribute whose values can change, but the changes are infrequent. For example, a customer's address might change, or an employee's department could be updated. Without proper handling, these changes can lead to inaccurate historical reporting. If you query sales from a year ago, you want to see sales attributed to the customer's address *at that time*, not their current address.

Common SCD Types

Several types of SCDs are commonly used:

• Type 0: Fixed Dimension: The attribute never changes.
• Type 1: Overwrite: The new value replaces the old value. History is lost.
• Type 2: Add New Row: A new row is added to the dimension table for each change. This preserves history.
• Type 3: Add New Attribute: A new column is added to store the previous value. Limited historical tracking.
• Type 4: Historical (using a history table): Changes are tracked in a separate history table.
• Type 5: Dual (Type 1 + Type 2): Maintains both the current and historical versions.
• Type 6: Hybrid (Type 1 + Type 3): Combines overwriting with tracking a previous value.

Implementing SCD Type 2 in SSAS (The Most Common)

SCD Type 2 is often the most useful for historical analysis. It requires modifications to the dimension table and specific configurations in SSAS.

Dimensional Table Setup

A typical Type 2 dimension table includes the following key elements:

• A natural key (e.g., CustomerID).
• A surrogate key (e.g., CustomerSK) as the primary key for the dimension table.
• Columns to track validity (e.g., StartDate, EndDate).
• A flag to indicate the current row (e.g., IsCurrent, often a boolean or a specific value like 'Y'/'N').
• The attribute(s) that change (e.g., Address, City, State).

Example SQL Table Structure:

CREATE TABLE DimCustomer (
    CustomerSK INT PRIMARY KEY IDENTITY(1,1),
    CustomerID INT NOT NULL, -- Natural key
    FirstName VARCHAR(50),
    LastName VARCHAR(50),
    Address VARCHAR(100),
    City VARCHAR(50),
    State VARCHAR(50),
    StartDate DATETIME NOT NULL,
    EndDate DATETIME,
    IsCurrent CHAR(1) NOT NULL DEFAULT 'Y'
);
                

SSAS Dimension Design

When processing your dimension in SSAS, you need to configure the attribute properties to support SCD Type 2.

1. Dimension Key: Use the surrogate key (CustomerSK) as the dimension key.
2. Attribute Properties:
   • For the attribute representing the changing value (e.g., Address), set the KeyColumns to the natural key (CustomerID).
   • Set the AttributeHierarchyEnabled property to True.
   • Crucially, configure the "Type" property of the dimension itself to "Slowly changing".
   • Within the Dimension Properties (right-click the dimension in Solution Explorer -> Properties):
     • HierarchyBasedOn: Set to Parent-Child or Organizational if applicable, otherwise AttributeBased.
     • KeyColumns: This should be your surrogate key (CustomerSK).
     • NameColumn: Can be a combination or a descriptive column.
     • Source: Your dimension table.
     • SlowlyChangingAttribute: Select the attribute that will track history (e.g., CustomerID).
     • Type: Set this to 2 - Add new row.
     • StartDate: Map to your StartDate column.
     • EndDate: Map to your EndDate column.
     • Status: Map to your IsCurrent column (SSAS will interpret values like 'Y'/'N' or 1/0).
3. Processing: Ensure your data source view correctly maps these columns. When you deploy and process the dimension, SSAS will automatically handle the logic for detecting changes and adding new rows based on your configurations.

Other SCD Types in SSAS

While Type 2 is prevalent, SSAS supports other types:

Type 1: Overwrite

This is the default behavior for most attributes. You simply map the attribute to its corresponding column in the dimension table. When the source data changes, SSAS will update the value during processing, losing historical context.

Type 3: Add New Attribute

To implement Type 3, you would add new columns to your dimension table (e.g., Address_Previous) and map them as separate attributes in SSAS. Your ETL process would be responsible for populating these historical columns.

Conclusion

Effectively managing slowly changing dimensions is vital for accurate historical analysis in your data warehouse and BI solutions. Analysis Services provides robust features, particularly for SCD Type 2, to simplify this complex task. By carefully designing your dimension tables and configuring SSAS attributes, you can ensure your data reflects historical changes accurately, enabling more insightful reporting and decision-making.