Relational Modeling in SQL Server Analysis Services

This document provides a comprehensive guide to relational modeling within SQL Server Analysis Services (SSAS). Relational modeling is a crucial step in building efficient and scalable data models for business intelligence solutions using SSAS.

Introduction to Relational Models in SSAS

In SSAS, a relational model (also known as a cube source or dimension data source) is typically built on top of a relational database, such as SQL Server. This model defines how data from your existing relational data sources will be represented and accessed within the multidimensional or tabular data models of SSAS.

Key Concepts

  • Data Source Views (DSVs): The primary interface for connecting to and preparing relational data for SSAS. DSVs allow you to select tables, define relationships, create calculated columns, and apply filters before using the data in a cube or tabular model.
  • Tables and Views: SSAS connects to tables and views in your relational database. It's often best practice to use pre-defined views to encapsulate complex business logic and ensure data consistency.
  • Relationships: Defining foreign key relationships between tables in the DSV is critical for SSAS to understand how to join data and build hierarchies.
  • Measures and Dimensions: While the raw data comes from relational sources, SSAS organizes this data into measures (numerical values to be aggregated) and dimensions (attributes used for slicing and dicing data). The relational model serves as the foundation for this organization.

Creating a Data Source View

The process of creating a relational model in SSAS begins with defining a Data Source View.

Steps:

  1. Open your SSAS project in SQL Server Data Tools (SSDT) or Visual Studio.
  2. Right-click on the Data Source Views folder and select New Data Source View.
  3. Follow the wizard to select an existing data source or create a new one, pointing to your relational database.
  4. Select the tables and views that contain the data relevant to your analysis. SSAS will often automatically detect relationships, but you should verify them.
  5. Use the DSV designer to rename columns, create calculated columns, define primary and foreign key relationships, and group tables into logical diagrams.

Best Practices:

  • Use views instead of base tables where possible to abstract complexity.
  • Define clear and accurate relationships between tables.
  • Normalize your data appropriately in the relational source.
  • Consider using a star or snowflake schema for optimal performance.
Tip: Rename columns and tables within the DSV to provide business-friendly names that are easier for end-users to understand.

Example: Sales Data Model

Consider a typical sales scenario with the following relational tables:

  • DimProduct (ProductKey, ProductName, Category, Subcategory)
  • DimCustomer (CustomerKey, CustomerName, City, Country)
  • DimDate (DateKey, FullDate, Year, Month, Day)
  • FactSales (SalesKey, ProductKey, CustomerKey, DateKey, Quantity, UnitPrice, SalesAmount)

In SSAS, you would create a DSV that includes these tables and establishes the relationships:

  • FactSales.ProductKey <-> DimProduct.ProductKey
  • FactSales.CustomerKey <-> DimCustomer.CustomerKey
  • FactSales.DateKey <-> DimDate.DateKey

From this DSV, you can then build:

  • Measures: SUM(FactSales.SalesAmount), SUM(FactSales.Quantity)
  • Dimensions: Product, Customer, Date hierarchies based on the corresponding tables.

Advanced Relational Modeling Techniques

Calculated Columns and Measures in DSVs

You can create calculated columns directly within the DSV. For example, in the FactSales table, you could create a calculated column for ExtendedPrice as Quantity * UnitPrice.

-- Example SQL for a calculated column
Quantity * UnitPrice

Key Relationships vs. Foreign Key Relationships

While SSAS often infers relationships from foreign keys, you can explicitly define key relationships in the DSV designer. This is especially useful for handling scenarios where foreign keys are not explicitly defined in the database or for performance tuning.

Managing Large Schemas

For databases with many tables, use the diagramming features in the DSV designer to group related tables logically. This improves readability and maintainability.

Conclusion

Relational modeling in SSAS is the foundational step for leveraging your existing data. By effectively creating and managing Data Source Views, you lay the groundwork for robust and performant multidimensional and tabular models, enabling powerful business intelligence and analytics.

For more detailed information, refer to the SSAS Modeling Basics and Tabular Modeling with SSAS sections.