Introduction to Multidimensional Modeling in Analysis Services
Welcome to the introduction to Multidimensional Modeling in Microsoft SQL Server Analysis Services (SSAS). This section will provide you with a foundational understanding of the concepts, components, and benefits of building multidimensional models for business intelligence solutions.
What is Multidimensional Modeling?
Multidimensional modeling is a technique used in business intelligence to represent data in a way that is optimized for analysis and querying. Unlike traditional relational databases that store data in two-dimensional tables, multidimensional models organize data into cubes, which allow for faster aggregation, slicing, and dicing of information.
Key characteristics of a multidimensional model include:
- Dimensions: These represent the perspectives or categories through which you analyze data (e.g., Time, Geography, Product).
- Measures: These are the quantitative values you want to analyze (e.g., Sales Amount, Quantity Sold, Profit).
- Facts: The underlying quantitative data that forms the basis of measures.
- Cubes: A central structure that contains facts and is organized by dimensions, providing a multidimensional view of the data.
Why Use Multidimensional Models?
Multidimensional models offer several significant advantages for BI and analytical reporting:
- Performance: Pre-aggregated data and optimized storage structures lead to significantly faster query responses compared to querying raw relational data.
- Ease of Use: Business users can intuitively understand and explore data through dimensions and measures, making complex data accessible.
- Rich Analytical Capabilities: Supports advanced analytical functions like OLAP (Online Analytical Processing) operations such as drill-down, roll-up, slice, and dice.
- Consolidated View: Integrates data from various sources into a single, consistent view for analysis.
Core Components of a Multidimensional Model
Understanding the fundamental building blocks is crucial:
- Databases: An Analysis Services database hosts your multidimensional models.
- Data Sources: Connections to your underlying relational databases or other data sources where your raw data resides.
- Data Source Views (DSVs): A logical representation of the data from your data sources, allowing you to rename objects, create joins, and define calculated columns without altering the source.
- Dimensions: As mentioned, these are the hierarchical structures used for analysis. Examples include Time (Year, Quarter, Month, Day), Geography (Country, State, City), and Product (Category, Subcategory, Product Name).
- Cubes: The core analytical object. A cube is a data structure that allows data to be modeled and analyzed from multiple perspectives (dimensions). It comprises measures and is organized by dimensions.
- Measure Groups: A collection of related measures within a cube, typically derived from a single fact table in the source data.
- Perspectives: User-defined subsets of a cube, allowing users to see only the dimensions, measures, and objects relevant to their specific role or task.
Note: While multidimensional models have been the standard for a long time, SQL Server Analysis Services also supports Tabular models, which offer a different approach based on relational in-memory technology. Choose the modeling approach that best suits your organization's needs.
Next Steps
In the following sections, we will delve deeper into each of these components:
- Designing and building effective Dimensions.
- Creating and configuring Cubes.
- Defining and managing Measures and KPIs.
- Implementing advanced Calculations and business logic.