Relationships in Multidimensional Models

This document describes how to define and manage relationships between dimensions and fact tables in SQL Server Analysis Services (SSAS) multidimensional models. Relationships are fundamental to connecting the descriptive attributes of your business (dimensions) with the numerical data you want to analyze (measures).

Types of Relationships

In SSAS, you typically define relationships between a fact table (which contains measures) and one or more dimension tables (which contain descriptive attributes).

Fact-Dimension Relationships

This is the most common type of relationship. It links a column in a dimension table to a column in a fact table. Analysis Services uses these relationships to aggregate measures based on dimension attributes.

• Foreign Key Relationship: The fact table contains a foreign key that references the primary key of the dimension table.

Dimension-Dimension Relationships (Implicit)

While not directly configured as a separate "relationship type" in the same way as fact-dimension links, relationships between dimensions are often established through shared dimension tables or through relationships defined within hierarchies. For example, if a "Product" dimension is related to a "Category" dimension, and both are linked to a "Sales" fact table, Analysis Services can infer how to slice sales by category.

Configuring Relationships in SQL Server Data Tools (SSDT)

When designing your multidimensional model in SSDT, you visually establish these relationships. This is typically done in the Dimension Designer or the Data Source View Designer.

Using the Data Source View Designer

The Data Source View Designer is where you define the logical structure of your data model. You can draw lines between tables to represent relationships.

1. Open your SSAS project in SQL Server Data Tools.
2. Navigate to the Data Source Views folder and open your data source view.
3. Drag your fact table(s) and dimension table(s) onto the diagram surface.
4. To create a relationship, right-click on the foreign key column in the fact table and select New Relationship.
5. Choose the corresponding primary key column in the dimension table.
6. Configure the relationship properties, such as join type (usually inner join for fact-dimension).

Relationship Properties

When defining a relationship, several properties are important:

• Join Type: Specifies how rows from the two tables are combined. For fact and dimension tables, an Inner Join is most common, ensuring that only facts with corresponding dimension attributes are included. A Left Outer Join might be used in specific scenarios, like including sales with no associated product information (though this usually indicates a data quality issue).
• Cardinality: This defines the number of related rows. For fact-dimension relationships, it's typically Many to One (many fact records to one dimension record).
• Name: A descriptive name for the relationship.

Referential Integrity

Analysis Services relies on referential integrity between your fact and dimension tables. This means that for every foreign key value in the fact table, there must be a corresponding primary key value in the dimension table. If referential integrity is broken (e.g., a fact record points to a non-existent dimension key), it can lead to incorrect aggregations or processing errors.

Handling Orphan Records

If your source data has orphan records (facts without matching dimensions), you have a few options:

• Cleanse the data at the source: The most recommended approach.
• Create a "Unknown" or "Not Applicable" member in the dimension: Configure Analysis Services to automatically group these orphan records into a specific dimension member.
• Use Left Outer Joins and filter: Less common and can impact performance.

Advanced Relationship Concepts

Junction Tables (Many-to-Many Relationships)

When a fact record can relate to multiple dimension records, and a dimension record can relate to multiple fact records, you use a junction table (or bridge table). This is common in scenarios like course enrollments or product sales to multiple customers in a single transaction.

In SSAS, you model many-to-many relationships by creating a separate junction table that links the two dimensions (e.g., linking a 'Student' dimension to a 'Course' dimension through an 'Enrollment' junction table). The fact table then relates to the junction table.

Degenerate Dimensions

These are dimensions that are derived directly from a fact table (e.g., a 'Ticket Number' from a sales fact table). You can model these as regular dimensions, but they don't have a separate dimension table in the RDBMS.

Impact on Performance

The design of your relationships significantly impacts cube processing time and query performance.

• Fewer, well-defined relationships generally lead to better performance.
• Avoid complex join paths in the data source view.
• Proper indexing in the relational source databases is critical.

By carefully defining and managing relationships, you build a robust and efficient multidimensional model that empowers users to gain deep insights from their data.