MSDN Documentation

Project Structure in Analysis Services Multidimensional Models

Understanding the project structure in SQL Server Analysis Services (SSAS) multidimensional models is crucial for efficient development, management, and deployment of your business intelligence solutions. A well-organized project ensures clarity, maintainability, and facilitates collaboration among team members.

Key Components of an SSAS Project

An SSAS multidimensional project, typically developed using SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects extension, is organized into several logical folders and files within the Solution Explorer. These represent different aspects of your multidimensional model.

• Data Sources: Defines the connections to your underlying relational data sources (e.g., SQL Server databases).
• Data Source Views: Represents a logical view of the data from one or more data sources. It allows you to create a unified, business-friendly view, perform basic transformations, and define relationships between tables.
• Dimensions: Contains the definition for all dimensions in your model (e.g., Time, Geography, Product).
• Cubes: The core of your multidimensional model, defining measures, dimensions, and hierarchies that users will query.
• Measure Groups: A logical grouping of related measures within a cube.
• Role Assignments: Defines security roles for users who will access the cube.
• Mining Structures: Used for data mining within Analysis Services (less common for pure multidimensional modeling).
• Actions: Custom actions that can be performed on cube data.
• Perspective: A subset of a cube that provides a focused view for specific user groups.
• Translations: Manages localized names and translations for cube objects.
• Deployment Targets: Configuration files for deploying the SSAS database to a server.

Typical Project Layout in Solution Explorer

When you create a new Analysis Services Multidimensional project, Solution Explorer in SSDT will present a structure similar to this:

Solution 'MyAnalysisProject' (1 project)
└── MyAnalysisProject (Project)
    ├── Data Sources
    │   └── [YourDataSource].ds
    ├── Data Source Views
    │   └── [YourDataSourceView].dsv
    ├── Dimensions
    │   ├── DimDate.dim
    │   ├── DimProduct.dim
    │   └── DimGeography.dim
    ├── Cubes
    │   └── [YourCube].cube
    │       ├── Measures
    │       ├── Dimensions
    │       ├── Hierarchies
    │       └── Translations
    ├── Measure Groups
    │   └── [YourMeasureGroup].mg
    ├── Roles
    │   └── [YourRole].role
    ├── Mining Structures (Optional)
    ├── Actions (Optional)
    ├── Perspectives (Optional)
    ├── Translations (Optional)
    ├── MyAnalysisProject.asdatabase (Project file)
    └── Deploy (.deploymenttargets)
            

Understanding Key Files and Folders

Data Sources (.ds)

Each file represents a connection string to an external data source. It's good practice to have separate data sources for different backend systems.

Data Source Views (.dsv)

These files define the logical schema. You can rename tables, columns, create calculated columns, and define relationships here, abstracting the physical schema from the multidimensional model itself.

Dimensions (.dim)

Each .dim file defines a dimensional object. This includes attributes, hierarchies, and member properties. Proper dimension design is fundamental to a performant and usable cube.

Cubes (.cube)

The .cube file is the central definition of your analytical model. It brings together measures, dimensions, and defines how they relate. Within the cube editor, you can further define calculations, perspectives, and other cube-specific properties.

Measure Groups (.mg)

Measure groups organize related measures, typically from the same fact table. This aids in managing complexity and optimizing performance.

Roles (.role)

Security is defined using roles, allowing granular control over data access at the dimension or cell level.

Best Practices

• Naming Conventions: Use consistent and descriptive naming conventions for all objects (data sources, views, dimensions, cubes, measures, attributes).
• Modularity: Break down complex models into smaller, manageable components. For example, separate dimensions and fact tables into distinct objects.
• Organization: Keep related objects together. For instance, all dimensions should be in the 'Dimensions' folder.
• Data Source Views: Leverage DSVs to create a clean, business-friendly layer over your data sources. Avoid directly querying source tables in cube definitions when possible.
• Comments and Documentation: Use descriptions and annotations within the SSAS project to explain design decisions.
• Version Control: Integrate your SSAS project with a version control system (like Git) for tracking changes and collaboration.