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Introduction

The Multidimensional model in Microsoft SQL Server Analysis Services (SSAS) provides a powerful framework for building OLAP cubes that enable fast, interactive analysis of large volumes of data.

This overview introduces the core components, architecture, and workflow for designing, developing, and deploying multidimensional solutions.

Key Concepts

ComponentDescription
CubeA logical container for measures, dimensions, and aggregations.
Measure GroupGroups related measures that share the same granularity.
DimensionDescribes perspectives for analyzing measure data (e.g., Time, Geography).
AttributeIndividual columns within a dimension that provide hierarchy levels.
KPIKey Performance Indicator that combines a measure with target, status, and trend.
AggregationPre‑computed summaries that accelerate query performance.

Architecture

The multidimensional engine consists of several layers that work together to provide fast query responses.

┌─────────────────────┐
│   Client Applications│
│ (Power BI, Excel…)   │
└─────────┬─────────────┘
          │
   ┌──────▼───────┐
   │   SSAS Server│
   │ (Multidimensional)│
   └──────┬───────┘
          │
   ┌──────▼───────┐
   │ Storage Engine│
   └──────┬───────┘
          │
   ┌──────▼───────┐
   │    Data      │
   │  Source (SQL)│
   └──────────────┘

Developing a Model

Follow these steps to build a multidimensional cube:

  1. Define the data source and data source view (DSV).
  2. Create dimensions and hierarchies.
  3. Design the cube, add measure groups, and associate dimensions.
  4. Configure aggregations and partitions.
  5. Implement calculations, KPIs, and security roles.
  6. Process the cube to load data.

Sample MDX query:

SELECT
  {[Measures].[Sales Amount]} ON COLUMNS,
  {[Date].[Calendar].[Calendar Year].Members} ON ROWS
FROM [Adventure Works]
WHERE ([Geography].[Country].&[United States])

Deployment

Deploy your solution using SQL Server Data Tools (SSDT) or PowerShell scripts. Recommended practices:

  • Use a dedicated deployment server.
  • Version your cube objects.
  • Automate processing schedules.
  • Monitor performance with SQL Server Profiler and DMVs.

Best Practices

  • Design dimensions with conformed hierarchies.
  • Limit granularity of measure groups to avoid unnecessary partitions.
  • Use aggregate designs to balance processing time vs. query performance.
  • Secure data with role‑based permissions at the dimension and cell level.
  • Regularly review and defragment indexes on the storage engine.

Further Resources

Official documentation and learning paths: