SQL Server Analysis Services

Multidimensional Modeling

Database Design in Multidimensional Models

Designing a robust and efficient multidimensional database is crucial for the performance and usability of your SQL Server Analysis Services (SSAS) solutions. This section covers the key considerations and best practices for designing your multidimensional models.

Understanding the Core Concepts

Multidimensional models are built around the concepts of cubes, dimensions, and measures. A cube is a data structure that allows for fast analysis of data. It is composed of:

  • Dimensions: These represent the different perspectives from which you can analyze your business data. Examples include Time, Geography, Product, and Customer. Dimensions are typically hierarchical, allowing users to drill down and roll up through various levels of detail.
  • Measures: These are the quantitative values that you want to analyze. They are usually numerical data such as Sales Amount, Quantity Sold, or Profit. Measures are aggregated from the fact data.
  • Fact Tables: These tables in your data warehouse contain the business events and metrics that you want to analyze. They typically have foreign keys that link to dimension tables.
  • Dimension Tables: These tables contain the descriptive attributes for each dimension.

Key Design Considerations

1. Star Schema vs. Snowflake Schema

The underlying structure of your relational data warehouse influences the multidimensional model design. The two most common schemas are:

  • Star Schema: A central fact table is directly connected to multiple dimension tables. This is generally preferred for SSAS as it simplifies the model and often leads to better query performance.
  • Snowflake Schema: Dimension tables are normalized into multiple related tables. While this reduces data redundancy in the relational database, it can introduce complexity and potentially slower query performance when translated to a multidimensional model.

For SSAS multidimensional modeling, a star schema is often the most straightforward and performant choice.

2. Dimension Design Best Practices

  • Granularity: Determine the lowest level of detail for each dimension. For example, the Time dimension might be at the day level, while the Product dimension could be at the SKU level.
  • Hierarchy: Define logical hierarchies within dimensions (e.g., Year > Quarter > Month > Day for Time). This enables drill-down and roll-up functionality.
  • Attributes: Include all relevant descriptive attributes for analysis. Avoid including excessively granular or irrelevant attributes in dimensions.
  • Surrogate Keys: Use surrogate keys (system-generated integers) as primary keys in dimension tables instead of natural keys from the source system. This handles changes in natural keys and improves performance.
  • Slowly Changing Dimensions (SCDs): Implement appropriate SCD strategies (Type 1, Type 2, etc.) to manage historical changes in dimension attributes.

3. Measure Group Design

Measure groups are collections of measures that share the same source fact table and granularity. Consider:

  • Granularity: Define the granularity of each measure group, which is dictated by the grain of its source fact table.
  • Aggregation: Plan how measures will be aggregated (Sum, Count, Average, Min, Max). SSAS can pre-aggregate measures for performance, so defining sensible aggregations is vital.
  • Measures: Define clear, meaningful measures. Avoid redundant or poorly defined measures.

4. Performance Optimization

Several factors contribute to the performance of your multidimensional model:

  • Aggregations: Properly designed and generated aggregations are the most significant factor for query performance.
  • Indexing: While SSAS manages its own storage, the underlying relational database structure (especially indexes on fact and dimension tables) can impact data processing speed.
  • Partitioning: Partitioning large fact tables can improve query performance and manageability.
  • Data Types: Use appropriate data types for measures and dimension attributes.

Example: Designing a Sales Cube

Consider a simple sales scenario. You might have a fact table named FactSales with columns like DateKey, ProductKey, StoreKey, SalesAmount, and QuantitySold.

You would then design dimension tables like:

  • DimDate (with attributes like Year, Quarter, Month, Day)
  • DimProduct (with attributes like ProductName, Category, Subcategory)
  • DimStore (with attributes like StoreName, City, State)

In SSAS, you would create a cube with a measure group based on FactSales, defining SalesAmount and QuantitySold as measures. The dimensions would be linked to their respective dimension tables.

A snippet of a conceptual MDX query could look like:


SELECT
    {[Measures].[Sales Amount], [Measures].[Quantity Sold]} ON COLUMNS,
    {[DimDate].[Calendar Year].Members * [DimProduct].[Category].Members} ON ROWS
FROM [SalesCube]
WHERE ([DimStore].[City].[New York])

Next Steps

After designing the database structure, you'll proceed to define dimensions and measures in detail. Understanding the relationships between your source data and your analytical requirements is paramount to a successful multidimensional model.