Analysis Services Multidimensional Models
On This Page
This documentation covers the intricacies of developing and managing multidimensional models in SQL Server Analysis Services (SSAS). Multidimensional models provide a powerful way to analyze business data, enabling complex queries and insightful reporting.
Introduction to Multidimensional Models
Multidimensional models in Analysis Services are built around the concept of a data cube. This structure is optimized for analytical queries, allowing users to slice, dice, drill down, and roll up data across various business dimensions. They are particularly well-suited for complex business intelligence scenarios where historical analysis and performance monitoring are critical.
Key Concepts
Understanding the core components of a multidimensional model is essential for effective development and usage.
Cubes
A cube is a data structure that contains measures and dimensions. It's the central element of a multidimensional model, providing a pre-aggregated and organized view of business data for analysis. Think of it as a multidimensional spreadsheet where you can explore data from different perspectives.
Dimensions
Dimensions represent the perspectives or categories through which you analyze your data. Common examples include Time, Geography, Product, and Customer. Each dimension contains attributes that further refine the analysis, such as Year, Quarter, Month for the Time dimension, or Country, State, City for the Geography dimension.
Measures
Measures are the quantitative values that you want to analyze. These are typically numeric data points, such as Sales Amount, Quantity Sold, Profit, or Budgeted Expense. Measures are aggregated across dimensions to provide summarized insights.
Hierarchies
Hierarchies define the levels of aggregation within a dimension. For example, in a Time dimension, you might have a hierarchy of Year > Quarter > Month > Day. This allows users to easily navigate from a high-level view (e.g., total sales for a year) to more granular details (e.g., sales for a specific month). Hierarchies are crucial for drill-down and roll-up operations.
KPIs (Key Performance Indicators)
KPIs are specific metrics defined in the model to track important business objectives. They often include a target value, an actual value, and a status indicator (e.g., good, bad, neutral). KPIs help users quickly assess business performance against goals.
Architecture
A multidimensional model in SSAS consists of several layers:
- Data Source Layer: Connects to relational data sources (e.g., SQL Server databases) or other sources.
- Data Source View Layer: A logical representation of the data from the data sources, allowing you to define relationships and transformations.
- Cube Layer: The core of the model, where dimensions, measures, calculations, and KPIs are defined.
- Client Layer: Tools and applications (e.g., Excel, Power BI, custom applications) that connect to and query the SSAS cube.
Development Steps
Developing a multidimensional model typically involves the following steps:
- Create a New Analysis Services Project: In Visual Studio with SQL Server Data Tools (SSDT).
- Define Data Sources: Configure connections to your underlying data.
- Create Data Source Views: Define the logical schema for your analysis.
- Create Dimensions: Build dimensions based on your data, defining attributes and hierarchies.
- Create Measures: Define measures and their aggregations.
- Create Cubes: Assemble dimensions and measures into cubes.
- Add Calculations and KPIs: Enhance the model with custom calculations and performance indicators.
- Process the Cube: Load data from the data sources into the multidimensional structure.
- Deploy the Cube: Make the cube available for querying.