Attributes in Multidimensional Modeling

In Analysis Services multidimensional models, attributes are fundamental building blocks that define the characteristics of data within your dimensions. They represent individual data points that users can use to slice, dice, and filter data in reports and analyses.

What are Attributes?

An attribute typically corresponds to a column in a dimension table. For example, in a 'Product' dimension, attributes might include 'Product Name', 'Product Category', 'Color', and 'Size'. Each attribute provides a specific way to categorize and analyze the underlying data.

Key Concepts and Properties

• Attribute Key: This is the primary identifier for an attribute. It's often a surrogate key from the source data that uniquely identifies each member of the attribute.
• Attribute Name: The user-friendly name of the attribute, displayed in client applications.
• Attribute Data Type: The data type of the attribute's values (e.g., String, Integer, Date).
• Attribute Hierarchy Levels: Attributes can be used to build hierarchies, providing drill-down capabilities.
• Attribute Type: Attributes can be classified as Grouping, Key, or Ragged-End.

Attribute Relationships

Attributes within a dimension can have relationships with each other, forming hierarchies. These relationships define how attributes are organized from a broad perspective to a granular one. For instance, in a 'Geography' dimension, you might have a hierarchy: Country > State > City. The 'State' attribute depends on the 'Country' attribute, and 'City' depends on 'State'.

Attribute Types

Analysis Services distinguishes between several attribute types, impacting their behavior and how they are used:

• Key Attribute: This is the primary attribute used to uniquely identify each member of a dimension. It is essential for linking dimension members to fact table records.
• Attribute: A standard attribute used for browsing and filtering data.
• Disallowed: Attributes marked as disallowed are not displayed to users and cannot be used in queries.

Creating and Managing Attributes

Attributes are typically created and managed within SQL Server Data Tools (SSDT) when you design your multidimensional cube. You define attributes based on columns from your dimension tables. Key properties you'll configure include:

• The source column from your dimension table.
• The attribute's name.
• Whether it's a key attribute.
• Its relationship to other attributes within the dimension.

Example: Product Dimension Attributes

Consider a 'Product' dimension with the following columns in its source table:

• ProductID (Integer, Primary Key)
• ProductName (String)
• CategoryName (String)
• SubcategoryName (String)
• BrandName (String)

In Analysis Services, you would define these as attributes:

• ProductID would likely be the Key Attribute.
• ProductName, CategoryName, SubcategoryName, and BrandName would be regular Attributes.

These attributes could then be organized into hierarchies, such as:

CategoryName > SubcategoryName > BrandName > ProductName

Best Practices

• Use surrogate keys as attribute keys for better performance and data management.
• Name attributes clearly and concisely.
• Organize attributes into meaningful hierarchies to enhance user navigation.
• Avoid overly wide dimensions with many attributes; consider breaking down large dimensions if necessary.
• Regularly review and optimize attribute relationships for performance.

Understanding and effectively utilizing attributes is crucial for building robust, performant, and user-friendly multidimensional models in Analysis Services.