Modeling with SQL Server Analysis Services

This section provides comprehensive guidance on designing and implementing data models in SQL Server Analysis Services (SSAS). Effective data modeling is crucial for creating efficient, scalable, and user-friendly analytical solutions.

Core Concepts in SSAS Modeling

SSAS enables the creation of sophisticated multidimensional or tabular data models that serve as the foundation for business intelligence and reporting. Key concepts include:

• Data Sources: Connecting to and defining the underlying relational or other data sources that will feed your analysis services models.
• Models: The structure of your analytical data. SSAS supports two primary model types:
  • Multidimensional Models: Based on cubes, dimensions, and measures, offering a rich OLAP experience.
  • Tabular Models: In-memory databases that use in-memory analytics engines and provide a relational-like data model, often preferred for self-service BI.
• Dimensions: Attributes that describe business entities and provide context for measures (e.g., Time, Geography, Product).
• Measures: Numerical values that represent business performance metrics (e.g., Sales Amount, Quantity Sold).
• Hierarchies: Structured relationships within dimensions that allow users to navigate data at different levels of granularity.
• Relationships: Defining how different tables or dimension attributes relate to each other to form a cohesive model.

Best Practices for Dimension Design

Well-designed dimensions are the backbone of a usable SSAS model. Consider these best practices:

• Attribute Granularity: Ensure each attribute in a dimension is at the lowest practical level of detail.
• Descriptive Attributes: Include relevant descriptive attributes that users will need for filtering and slicing data.
• User-Friendly Naming: Use clear and intuitive names for dimensions, hierarchies, and attributes.
• Attribute Relationships: Define attribute relationships within dimensions to enable parent-child hierarchies and snowflake schemas.

Designing Effective Measures

Measures provide the quantitative insights users seek. Focus on:

• Aggregation Types: Choose appropriate aggregation functions (SUM, COUNT, AVERAGE, MIN, MAX) for your measures.
• Calculated Measures: Create complex calculations using DAX (for Tabular) or MDX (for Multidimensional) to derive new insights.
• Measure Grouping: Organize measures logically into measure groups for better usability.

Working with Tabular Models

Tabular models offer a more modern and agile approach to data modeling, especially for self-service BI scenarios. They leverage the Power Pivot data model technology and are queried using DAX.

• DAX (Data Analysis Expressions): The formula language used to create calculated columns, measures, and define row-level security in tabular models.
• Relationships: Visual representation and definition of relationships between tables in a tabular model.
• Import vs. DirectQuery: Understand the trade-offs between importing data into memory for faster performance versus using DirectQuery to query the source database directly.

Working with Multidimensional Models

Multidimensional models, built around cubes, provide a traditional OLAP experience with robust slicing and dicing capabilities. They are typically queried using MDX.

• Cubes: The central construct of a multidimensional model, containing measures and dimensions.
• MDX (Multidimensional Expressions): The query language used to retrieve data from multidimensional cubes.
• Partitions: Optimize performance by dividing large cubes into smaller, manageable partitions.

Example: Creating a Sales Fact Table Relationship

In both Tabular and Multidimensional models, you'll define relationships between fact tables (containing measures) and dimension tables.

-- Example conceptual SQL for creating a relationship (actual implementation varies by tool)
CREATE RELATIONSHIP FactSales.ProductID TO DimProduct.ProductID ON (SYNCHRONIZED);
-- Or for Multidimensional, within the dimension perspective:
-- Define a relationship from the Product dimension to the Sales fact table using the ProductID key.

Next Steps

Continue exploring the following topics to deepen your understanding of SSAS modeling:

• Configuring Data Sources
• Advanced Dimension Design Techniques
• Creating Complex Measures with DAX/MDX