Overview of Attributes

This document provides a comprehensive overview of attributes within the context of SQL Server Analysis Services (SSAS) multidimensional modeling. Attributes are fundamental building blocks that define the characteristics and hierarchical structure of dimensions.

What are Attributes?

In a multidimensional data model, an attribute represents a single characteristic or property of a business entity. For example, in a 'Customer' dimension, attributes might include 'CustomerID', 'CustomerName', 'City', 'State', and 'Country'. These attributes are used to slice, dice, and filter data in queries, enabling users to analyze information from various perspectives.

Key Concepts:

• Attribute Hierarchy: Attributes are often organized into hierarchies, allowing users to navigate through different levels of detail. For instance, a 'Geography' hierarchy might consist of 'Country' (top level), 'State' (middle level), and 'City' (lowest level).
• Attribute Relationship: Attributes within a dimension can have relationships defined between them. This is crucial for establishing the logical structure of the hierarchy and ensuring data integrity. For example, 'City' is related to 'State', and 'State' is related to 'Country'.
• Attribute Types: SSAS supports various attribute types, each with specific properties and behaviors, such as 'Regular', 'Key', 'Diski', and 'Unrestricted'.

Creating and Managing Attributes

Attributes are typically defined and managed within the dimension objects in your SSAS project. When you create a new dimension, you specify the source columns from your data warehouse that will serve as attributes. You can then configure their properties, such as names, data types, and relationships.

Attribute Properties:

Each attribute has a set of properties that control its behavior and how it is presented to users. Some of the most important properties include:

• Name: The display name of the attribute.
• Attribute Hierarchy Name: The name of the hierarchy to which the attribute belongs.
• Attribute Type: Defines the role and behavior of the attribute (e.g., Key, Regular).
• Key Columns: The column(s) in the data source that uniquely identify the attribute members.
• Name Column: The column that contains the display name for attribute members.
• Sort Order: Determines how attribute members are sorted.

Attribute Relationships in Detail

Attribute relationships are the backbone of dimension hierarchy design. They define how attributes relate to each other, enabling SSAS to correctly process and aggregate data.

• Natural Hierarchy: Relationships defined by the natural structure of the data. For example, a customer's city logically belongs to a state.
• Granularity: The lowest level of detail in a hierarchy. For example, 'Product Code' might be the lowest level in a 'Product' dimension.

Types of Attribute Relationships:

• Parent-Child: Used for representing hierarchical data where the parent-child relationship is explicitly defined in the source data (e.g., organizational charts).
• Ranged: Used for grouping data based on numerical ranges (e.g., age groups).

Best Practices

• Ensure that each attribute has a unique key.
• Use meaningful names for attributes.
• Define attribute relationships accurately to reflect the business logic.
• Consider the granulate of your dimensions and attributes for efficient analysis.

For more in-depth information on specific attribute configurations and advanced scenarios, please refer to the related documentation on Creating Attributes and Attribute Relationships.