Key Design Considerations

1. Data Modeling

The foundation of any successful Analysis Services project lies in a well-designed data model. This involves understanding your business requirements and translating them into efficient cube structures.

  • Dimensional Modeling: Embrace the star or snowflake schema for your data warehouse. This structure is highly optimized for analytical queries.
  • Fact Tables: Choose the appropriate grain for your fact tables. A fine-grained fact table offers more detail but can lead to larger cube sizes.
  • Dimension Design: Consider dimension types (standard, degenerate, slowly changing dimensions). Implement proper hierarchies for intuitive navigation.
  • Measures: Define measures carefully, considering aggregation methods (Sum, Count, Average, Min, Max). Use calculated measures and KPIs to provide business-specific insights.

2. Performance Optimization

Performance is paramount for user adoption. Several factors influence how quickly users can explore data.

  • Partitioning: Divide large fact tables into smaller, manageable partitions based on time or other criteria. This improves query performance and management.
  • Aggregation Design: Pre-calculate aggregations for frequently accessed data combinations. This significantly speeds up query responses. Use the Aggregation Design Wizard or custom aggregation strategies.
  • Indexing: While not explicit in Analysis Services, the underlying data warehouse indexing strategy plays a crucial role. Ensure fact and dimension tables are appropriately indexed.
  • Query Optimization: Write efficient MDX queries. Avoid large sub-cube operations where possible.

3. Scalability and Manageability

Design for growth and ease of maintenance.

  • Hardware Resources: Allocate sufficient CPU, RAM, and fast I/O for your Analysis Services instance.
  • Instance Sizing: Plan for future data growth and user concurrency.
  • Deployment Strategy: Utilize project-based development with Visual Studio and deploy using XMLA scripts or deployment utilities.
  • Backup and Restore: Implement a robust backup strategy for your databases.

4. Security Considerations

Protect your sensitive data and ensure users have appropriate access.

  • Role-Based Security: Define roles with specific permissions on cubes, dimensions, and cells.
  • Row-Level Security: Implement security at the row level within dimensions to restrict data visibility based on user attributes.
  • Server Roles: Assign administrative privileges carefully.

5. User Experience

Make it easy for users to interact with the data.

  • Intuitive Hierarchies: Structure dimension hierarchies logically.
  • Clear Naming Conventions: Use descriptive names for cubes, dimensions, attributes, and measures.
  • Action/Action-Based Calculations: Embed drill-through actions or URLs to provide deeper insights or navigate to related content.

Example Cube Structure (Conceptual)

Cube: SalesCube

Dimensions:
  - DimDate (Date Hierarchy: Year -> Quarter -> Month)
  - DimProduct (Product Hierarchy: Category -> Subcategory -> Product)
  - DimCustomer (Customer Hierarchy: Country -> City -> Customer)
  - DimSalesTerritory

Fact Table: FactSales
  - Grain: One row per product sale transaction

Measures:
  - [Total Sales Amount]: SUM(FactSales.SalesAmount)
  - [Quantity Sold]: SUM(FactSales.OrderQuantity)
  - [Average Sale Price]: AVG(FactSales.SalesAmount)
  - [Number of Orders]: COUNT(DISTINCT FactSales.OrderID)

Calculated Measures:
  - [Sales Growth %]: ([Total Sales Amount] - PREVIOUSQUARTER([Total Sales Amount])) / PREVIOUSQUARTER([Total Sales Amount])