Designing Hierarchies in Multidimensional Modeling

Hierarchies are fundamental to multidimensional modeling in SQL Server Analysis Services (SSAS). They represent relationships between attributes in a dimension, allowing users to navigate and analyze data at different levels of granularity. Effective hierarchy design is crucial for creating intuitive and performant cubes.

What is a Hierarchy?

A hierarchy is a set of attributes organized in a parent-child relationship, forming a top-down structure. For example, in a 'Geography' dimension, you might have a hierarchy like 'Continent' > 'Country' > 'State/Province' > 'City'. Users can then drill down from the continent level to see data for individual countries, and further down to cities.

Types of Hierarchies

SSAS supports two primary types of hierarchies:

Ragged Hierarchies: In a ragged hierarchy, levels do not necessarily align across all branches. For instance, a 'Product Category' hierarchy might have 'Department' > 'Category' > 'Subcategory'. Some departments might not have subcategories, leading to uneven depths.
Unbalanced Hierarchies: These hierarchies have varying depths for different members within the same level. For example, a 'Manager' hierarchy where some employees report directly to the top-level manager, while others have multiple layers of management above them.
Balanced Hierarchies: All branches of a balanced hierarchy have the same depth. This is the simplest type of hierarchy, often seen in organizational structures or date hierarchies.

Creating Hierarchies in Visual Studio

You can create hierarchies using SQL Server Data Tools (SSDT) within Visual Studio.

Open your SSAS project in Visual Studio.
In the Solution Explorer, double-click the dimension you want to add a hierarchy to.
Navigate to the 'Browser' tab.
From the 'Toolbox', drag and drop attributes onto the design surface to form your hierarchy. You can arrange them by dragging them into the desired order.
To create a new hierarchy, right-click on the dimension and select 'New Hierarchy'. Choose the type of hierarchy (e.g., User Hierarchy).
Name your hierarchy appropriately.
Drag and drop attributes from the 'Data Source View' or 'Attributes' pane into the hierarchy structure.

Screenshot of SSAS Hierarchy Design in Visual Studio

Visual representation of hierarchy design in SSDT.

Best Practices for Hierarchy Design

Meaningful Naming: Use clear and descriptive names for hierarchies and their levels.
Attribute Granularity: Ensure that the lowest level of your hierarchy corresponds to the grain of your fact table for accurate analysis.
Attribute Relationships: Define correct attribute relationships to optimize query performance. A 'Many-to-One' relationship is typical from a lower-level attribute to a higher-level attribute in a hierarchy.
User-Defined Hierarchies: Create user-defined hierarchies to expose business-relevant navigation paths that might not exist as direct relationships in the underlying data source.
Natural Hierarchies: Leverage natural hierarchies present in your data, such as date hierarchies (Year, Quarter, Month, Day) or geographical hierarchies.
Avoid Overly Deep Hierarchies: Very deep hierarchies can be difficult for users to navigate. Consider breaking down complex structures.
Regularity: Aim for balanced or predictable raggedness where possible to improve user experience and performance.

Note: When designing hierarchies, remember to consider the end-user experience. The goal is to provide intuitive ways to slice and dice data.

Example: Date Hierarchy

A common and essential hierarchy is the date hierarchy. This allows users to analyze trends over time.

Year
  Quarter
    Month
      Day
            

In SSAS, you can often use the built-in date dimension features or create custom date dimensions with these hierarchies automatically generated or manually defined.

Key Considerations

Performance: Well-designed hierarchies with appropriate attribute relationships significantly impact query performance.
Usability: Intuitive hierarchies make it easier for business users to understand and explore their data.
Maintainability: Clear and logical hierarchy design simplifies future updates and modifications to your cube.

By carefully designing hierarchies, you can unlock the full analytical potential of your SQL Server Analysis Services cubes, empowering users with deep insights into their business data.