Understanding Multidimensional Models
SQL Server Analysis Services (SSAS) Multidimensional models provide a robust framework for building Online Analytical Processing (OLAP) cubes. These cubes are designed for fast querying of large datasets, enabling business users to explore data from various perspectives and gain insights quickly.
Core Concepts
Explore the fundamental building blocks: Cubes, Dimensions, Hierarchies, Attributes, Measures, and KPIs.
Learn More →Dimension Design
Discover best practices for designing dimensions, including handling slowly changing dimensions and creating user-friendly hierarchies.
Learn More →Measure Design
Understand how to define and aggregate measures effectively to answer critical business questions.
Learn More →MDX Fundamentals
Get started with Multidimensional Expressions (MDX), the powerful query language for SSAS models.
Learn More →Core Concepts Explained
Multidimensional models are built upon several key components that work together to facilitate complex data analysis.
Cubes
The central data structure, representing a business process or subject area. It contains dimensions and measures.
Dimensions
Categorical data that allows users to slice and dice facts. Examples include Time, Geography, Product, and Customer.
Hierarchies
Ordered levels within a dimension that enable drill-down and roll-up analysis. E.g., Year > Quarter > Month > Day.
Measures
Numeric, additive values that represent business metrics, such as Sales Amount, Quantity Sold, or Profit.
Attributes
Properties of a dimension member. E.g., for a 'Product' dimension, attributes could be 'Product Name', 'Color', 'Size'.
KPIs
Key Performance Indicators, which are measures with specific targets and statuses, providing quick insights into business performance.
Dimension Design Best Practices
Well-designed dimensions are crucial for intuitive and efficient analysis.
- Descriptive Attributes: Include attributes that users will want to group or filter by.
- Natural Hierarchies: Define hierarchies that reflect the business structure (e.g., Country -> State -> City).
- Parent-Child Hierarchies: Use for organizational structures or indeterminate levels (e.g., employee reporting lines).
- Snowflake vs. Star Schema: Understand the trade-offs between these schemas for dimension normalization.
- Attribute Relationships: Define relationships between attributes to optimize query performance and ensure data integrity.
Measure Design Principles
Effective measure design ensures that your analytical data accurately reflects business reality.
- Aggregability: Measures should be consistently aggregatable across all dimensions.
- Calculation Types: Understand implicit (COUNT, SUM) vs. explicit (calculated measures) aggregations.
- Base Measures: Define raw facts from the data source.
- Calculated Measures: Create complex calculations using MDX (e.g., Profit Margin = ([Measures].[Profit] / [Measures].[Sales Amount]) * 100).
- Currency and Formatting: Properly format measures for clarity (e.g., currency symbols, number of decimal places).
MDX Fundamentals for Analysis
Multidimensional Expressions (MDX) is the standard query language for SSAS. It's essential for retrieving data and performing complex calculations.
Here's a basic MDX query to retrieve sales amount by year:
SELECT
{[Measures].[Sales Amount]} ON COLUMNS,
[Date].[Calendar Year].[Calendar Year].MEMBERS ON ROWS
FROM
[YourCubeName]
Key MDX Concepts:
- Sets: Collections of members (e.g.,
[Date].[Calendar Year].[Calendar Year].MEMBERS). - Tuples: Ordered lists of members, one from each dimension (e.g.,
([Measures].[Sales Amount], [Date].[Calendar Year].&[2023])). - Functions: Powerful functions for navigation, aggregation, and manipulation (e.g.,
SUM,AVG,HEAD,TAIL). - Calculated Members: Define new measures or members dynamically within a query.
Mastering MDX unlocks the full potential of your Analysis Services models.
Explore the Full MDX Guide →