Introduction to the Multidimensional Model
The SQL Server Analysis Services (SSAS) multidimensional model provides a robust and high-performance platform for business intelligence solutions. It allows you to build complex analytical models that can be queried using Multidimensional Expressions (MDX) and accessed by various client tools such as Microsoft Excel, Power BI, and custom applications.
This documentation guides you through the concepts, design, and implementation of multidimensional models in SSAS. We'll cover everything from the fundamental building blocks of cubes and dimensions to advanced topics like MDX query language, performance tuning, and security.
Core Concepts
Understanding the core components of the multidimensional model is crucial for effective design and utilization.
Cubes
A cube is a data structure that stores aggregated data from various sources, optimized for slicing, dicing, and drilling down into business information. It's comprised of dimensions and measures.
Dimensions
Dimensions provide the context for the data. They represent the 'who,' 'what,' 'where,' 'when,' and 'how' of your business. Common examples include Time, Geography, Product, and Customer.
Measures
Measures are the numerical data that you want to analyze. They are typically facts or metrics, such as Sales Amount, Quantity Sold, or Profit. Measures are aggregated within cubes.
Hierarchies
Hierarchies represent the levels of aggregation within a dimension. For example, a Time dimension might have a hierarchy with levels like Year, Quarter, Month, and Day.
Attributes
Attributes are the descriptive characteristics of a dimension. For instance, in a Product dimension, attributes might include Product Name, Color, and Size.
Perspectives
Perspectives allow you to present different subsets of a cube to different users, simplifying the view and tailoring it to specific business roles.
Translating User Interface Elements
SSAS supports the translation of dimension and measure names, descriptions, and attribute values, enabling a multilingual user experience.
Designing a Multidimensional Model
The design phase is critical for creating a performant and user-friendly analytical model.
Planning Your Model
Before you begin building, thoroughly understand the business requirements, identify key performance indicators (KPIs), and define the dimensions and measures needed.
Creating a New Project
Use SQL Server Data Tools (SSDT) or Visual Studio with the Analysis Services projects extension to create a new Analysis Services Multidimensional Project.
Data Sources
Configure connections to your operational data sources (e.g., SQL Server, Oracle) from which you will extract data.
Data Source Views
A Data Source View (DSV) is a logical representation of your relational data, allowing you to define relationships, name mappings, and derived columns.
Designing Cubes
Define measures, add dimensions, configure measure groups, and set up cube properties.
Designing Dimensions
Create dimensions from your DSV, define hierarchies, and configure attribute properties.
Designing Measures
Define aggregations for your measures (e.g., Sum, Count, Average) and configure properties like format strings.
Calculations and KPIs
Use MDX to create calculated measures and members. Define Key Performance Indicators (KPIs) to track business objectives.
Security
Implement role-based security at the cube, dimension, or cell level to control data access.
Deployment
Deploy your SSAS project to an Analysis Services server.
MDX (Multidimensional Expressions)
MDX is the query language used to retrieve data from multidimensional models. It is a powerful and expressive language for OLAP analysis.
MDX Basics
Learn about the fundamental syntax, including SELECT statements, axis specification, and set notation.
MDX Functions
Explore the rich set of built-in MDX functions for manipulating data, performing calculations, and defining complex logic.
Writing MDX Queries
Examples of common MDX queries for slicing, dicing, drilling down, and performing ad-hoc analysis.
SELECT
{[Measures].[Internet Sales Amount]} ON COLUMNS,
{[Date].[Calendar Year].Members} ON ROWS
FROM [Adventure Works DW]
WHERE ([Product].[Category].[Bikes])Administration and Maintenance
Learn about managing your SSAS database, including processing, backups, and monitoring.
Performance Tuning
Strategies and techniques for optimizing the performance of your multidimensional models, including aggregations, indexing, and partitioning.
Troubleshooting
Common issues and solutions encountered when working with SSAS multidimensional models.