Developing Multidimensional Models in SQL Server Analysis Services

This section provides comprehensive guidance on designing, developing, and managing multidimensional models in SQL Server Analysis Services (SSAS). Multidimensional models are a powerful way to represent complex business data for analytical purposes, enabling users to explore data through dimensions, measures, hierarchies, and calculations.

Introduction to Multidimensional Models

Multidimensional models, often referred to as OLAP (Online Analytical Processing) cubes, provide a flexible and high-performance environment for business intelligence. They are built upon a foundation of dimensions and facts, allowing users to slice and dice data from various perspectives.

Key Concepts

• Dimensions: Attributes that describe the business context of data, such as time, geography, or product.
• Measures: Numerical values that can be aggregated, such as sales amount, quantity, or profit.
• Hierarchies: Organized structures within dimensions that allow for drill-down and roll-up operations (e.g., Year > Quarter > Month > Day).
• Cubes: The central analytical object in SSAS, composed of measures and dimensions.
• Partitions: Subdivisions of cube data that improve query performance and manageability.

Designing Your Multidimensional Model

A well-designed multidimensional model is crucial for effective business intelligence. Consider the following aspects during the design phase:

Star Schema vs. Snowflake Schema

Understand the trade-offs between star schema (simpler, faster for queries) and snowflake schema (more normalized, can reduce redundancy but increase query complexity) in the context of your data warehouse.

Dimension Design Best Practices

• Attribute Granularity: Define the level of detail for each attribute.
• Snowflaking: Decide when to normalize dimension attributes.
• Parent-Child Hierarchies: Implement for organizational structures or self-referencing data.
• Degenerate Dimensions: Handle transactional identifiers directly within the fact table.

Measure Design and Aggregation

Choose appropriate aggregation functions (e.g., Sum, Count, Average, Min, Max) for your measures. Understand the impact of measure types on performance and accuracy.

Developing with SQL Server Data Tools (SSDT)

SQL Server Data Tools (SSDT) is the primary IDE for developing SSAS multidimensional models. You'll use it to connect to your data sources, define dimensions, measures, and the cube structure.

Steps for Development

1. Create a New Analysis Services Project: Start by creating a new project in SSDT.
2. Configure Data Sources: Connect to your relational databases or other supported data sources.
3. Design Dimensions: Create and configure dimensions based on your star or snowflake schema.
4. Design Measures: Define measures and their aggregation properties.
5. Create the Cube: Assemble dimensions and measures into a cube.
6. Define Hierarchies and Relationships: Structure your dimensions for effective analysis.
7. Write MDX Expressions: Create calculated members and KPIs using Multidimensional Expressions (MDX).
8. Deploy and Process: Deploy your model to an SSAS server and process the data.

Multidimensional Expressions (MDX)

MDX is the query language used for retrieving data from SSAS multidimensional models. It's a powerful language for complex calculations, slicing, dicing, and aggregations.

MDX Fundamentals

• SELECT Statement: The core of any MDX query, used to specify axes and filters.
• Sets: Collections of members, essential for defining axes and filtering.
• Members: Individual items within a hierarchy or dimension.
• Calculated Members: Define custom measures or members on the fly.
• Functions: A rich set of functions for data manipulation, aggregation, and conditional logic.

SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    {[Date].[Calendar Year].Members} ON ROWS
FROM
    [Adventure Works DW]
WHERE
    ([Product].[Category].&[Bikes], [Geography].[Country].&[United States])

Performance Tuning and Best Practices

Optimizing your multidimensional models is crucial for providing a responsive analytical experience.

Key Tuning Strategies:

• Partitioning: Divide large cubes into smaller, manageable partitions.
• Aggregation Design: Proactively create aggregations to speed up common queries.
• Indexing: Understand how SSAS uses indexes for performance.
• Caching: Leverage SSAS caching mechanisms.
• MDX Optimization: Write efficient MDX queries.

Related Topics

• Developing Tabular Models
• Multidimensional Expressions (MDX) Reference
• Managing Analysis Services