Multidimensional Model Development
This section guides you through the process of developing multidimensional models in SQL Server Analysis Services (SSAS). Multidimensional models are a core component of business intelligence solutions, providing a robust and flexible way to analyze data.
Understanding Multidimensional Models
Multidimensional models organize data in a way that is optimized for analytical queries. They are built around cubes, which consist of:
- Measures: Numerical data that can be aggregated, such as sales amounts, quantities, or counts.
- Dimensions: Attributes that describe the measures, allowing users to slice and dice data. Examples include Time, Geography, or Product.
These elements are structured into hierarchies within dimensions, enabling drill-down and roll-up analysis.
Key Components of Multidimensional Model Development
1. Designing the Cube Structure
The first step is to define the business requirements and translate them into a logical cube structure. This involves identifying:
- Fact Tables: Contain the measures.
- Dimension Tables: Contain the attributes for dimensions.
- Relationships: How fact and dimension tables are related.
2. Creating Dimensions
Dimensions provide the context for your data. You can create dimensions from existing tables in your data source. Key aspects include:
- Attribute Design: Defining the attributes that will be used for analysis.
- Hierarchy Creation: Organizing attributes into meaningful hierarchies (e.g., Year > Quarter > Month > Day for a Time dimension).
- Dimension Types: Standard, Role-Playing, Snowflake, etc.
3. Creating Measures and Measure Groups
Measures are the quantitative data points you want to analyze. They are typically stored in fact tables.
- Measure Definition: Specifying the aggregation function (e.g., Sum, Count, Average) and data type.
- Measure Groups: Collections of related measures that share a common set of dimensions.
4. Processing and Deploying the Cube
Once the cube is designed, it needs to be processed to load data into the multidimensional structure. After processing, the cube can be deployed to the SSAS server for client applications to access.
5. Scripting and Advanced Features
SSAS supports advanced features that enhance cube functionality:
- Calculated Members: Defining custom measures or members that are derived from existing data.
- Kpis (Key Performance Indicators): Tracking and measuring progress against business objectives.
- Perspectives: Creating customized views of the cube for different user groups.
- Mining Dimensions: Integrating data mining models with multidimensional models.
Tools for Development
The primary tool for developing multidimensional models is SQL Server Data Tools (SSDT), which integrates with Visual Studio. SSDT provides a rich development environment for:
- Designing, creating, and managing SSAS projects.
- Working with databases, dimensions, measure groups, and cubes.
- Configuring security settings.
- Deploying solutions.
Best Practices
To ensure efficient and performant multidimensional models, consider these best practices:
- Star Schema: Design your data warehouse using a star schema for optimal performance.
- Naming Conventions: Use clear and consistent naming conventions for all objects.
- Data Types: Use appropriate data types for measures and attributes.
- Partitioning: Implement partitioning for large fact tables to improve query performance and manageability.
- Aggregations: Define appropriate aggregations to pre-calculate frequently accessed data.