Multidimensional Modeling Design Basics

This document provides fundamental principles and best practices for designing multidimensional models in SQL Server Analysis Services (SSAS). A well-designed multidimensional model is crucial for efficient data analysis, reporting, and business intelligence applications.

Key Concepts

Multidimensional modeling, often referred to as OLAP (Online Analytical Processing), organizes data into cubes that allow users to analyze data from various business perspectives. The core components of a multidimensional model are:

• Dimensions: These represent the descriptive attributes or hierarchies of your business. Examples include Time, Geography, Products, and Customers. Dimensions provide the context for your data.
• Measures: These are the numerical values that users want to analyze. They are typically aggregated from fact tables. Examples include Sales Amount, Quantity Sold, and Profit.
• Cubes: A cube is a data structure that contains measures and is organized around dimensions. It's the central object for analysis in SSAS.
• Hierarchies: Within dimensions, hierarchies allow users to navigate data at different levels of granularity (e.g., Year -> Quarter -> Month -> Day in a Time dimension).

Design Principles

Adhering to these principles will result in a robust and user-friendly multidimensional model:

1. Understand Business Requirements

Before any design work begins, thoroughly understand the business questions your model needs to answer. Interview stakeholders, review existing reports, and identify key performance indicators (KPIs) that need to be tracked.

2. Model Around Business Processes

Multidimensional models should align with business processes rather than just the structure of source data. For example, a sales process might involve sales orders, shipments, and returns, each potentially contributing to measures within a Sales cube.

3. Design Dimensions Carefully

Dimensions are the backbone of your analysis:

• Granularity: Ensure each dimension has the appropriate level of detail. For instance, a Product dimension might include attributes like Product Name, Category, Subcategory, and Brand.
• Attributes: Include descriptive attributes that users will want to filter, group, or label by.
• Hierarchies: Define meaningful hierarchies to enable natural drill-down and roll-up analysis.
• Skipped Levels and Ragged Hierarchies: Handle situations where not all parent-child relationships are uniform.
• Parent-Child Hierarchies: Useful for organizational structures like employee reporting lines.

4. Define Measures Appropriately

Measures should represent quantifiable business metrics:

• Aggregation: Determine the correct aggregation function (Sum, Count, Average, Min, Max) for each measure.
• Semi-Additive Measures: Understand how measures behave across time (e.g., account balances).
• Calculated Measures: Create measures based on existing measures to derive new insights (e.g., profit margin = Profit Amount / Sales Amount).

5. Choose the Right Cube Structure

Decide on the granularity of your fact tables and how they relate to dimensions. Consider using:

• Star Schema: A central fact table surrounded by dimension tables. This is the most common and efficient structure.
• Snowflake Schema: A more normalized structure where dimension tables are further normalized into sub-dimension tables. This can sometimes be less performant for OLAP queries.

6. Implement Performance Best Practices

A performant model is crucial for user adoption:

• Aggregations: Pre-calculate summaries of data to speed up queries.
• Partitioning: Divide large fact tables into smaller, more manageable partitions (e.g., by time).
• Indexing: Ensure underlying relational database tables are properly indexed.
• Data Types: Use appropriate data types in your source and SSAS models.

Example: Designing a Sales Cube

Let's consider a simplified sales scenario:

Fact Table: `FactSales` (with columns like `SalesAmount`, `Quantity`, `OrderDateKey`, `ProductKey`, `CustomerKey`)

Dimensions:

• Date Dimension: With hierarchies like `Year` -> `Quarter` -> `Month` -> `Day`.
• Product Dimension: With hierarchies like `Category` -> `Subcategory` -> `Product`.
• Customer Dimension: With attributes like `Customer Name`, `Country`, `City`.

Measures:

• `Sales Amount` (Sum)
• `Quantity Sold` (Sum)
• `Average Sale Price` (Calculated Measure: [Sales Amount] / [Quantity Sold])

By combining these elements in a cube, users can analyze total sales by product category for a specific quarter, or see the average sale price for a particular customer in a given country.

Next Steps

Once the basic design is established, you can move on to more advanced topics such as defining relationships, creating KPIs, and implementing security. Refer to the following sections for more detailed information.