Getting Started with Multidimensional Models in SQL Server Analysis Services

What are Multidimensional Models?

Multidimensional models, also known as OLAP (Online Analytical Processing) cubes, provide a powerful way to analyze business data. They are designed for fast querying and aggregation of large datasets, enabling users to gain insights into trends, patterns, and performance metrics.

Key components of a multidimensional model include:

• Cubes: The central data structure, representing a business process or subject area.
• Dimensions: Hierarchical structures that provide context to the data (e.g., Time, Geography, Product).
• Measures: Numerical values that can be aggregated (e.g., Sales Amount, Quantity Sold, Profit).
• Hierarchies: Levels within a dimension that allow for drill-down and roll-up analysis.

Steps to Create a Multidimensional Model

1. Define Your Data Source

Your multidimensional model will be built upon data residing in a relational data source, typically a SQL Server database. You'll need to establish a connection to this source within SSAS.

1. Open SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects.
2. Create a new Analysis Services project.
3. Right-click on "Data Sources" in the Solution Explorer and select "New Data Source".
4. Configure the connection string to your relational database.

2. Define Your Data Source Views

A Data Source View (DSV) acts as an abstraction layer over your relational data. It allows you to define the tables, views, and relationships that will be used in your cube, and to perform transformations if necessary.

1. Right-click on "Data Source Views" in the Solution Explorer and select "New Data Source View".
2. Select the data source you created in the previous step.
3. Drag and drop the necessary tables and views from your relational database schema into the DSV designer.
4. Define relationships between tables if they are not already present.

3. Create a Cube

The cube is where your multidimensional model comes to life. You'll define its structure based on the DSV.

1. Right-click on "Cubes" in the Solution Explorer and select "New Cube".
2. Use the Cube Wizard to select tables from your DSV that will contribute measures and dimensions.
3. For each dimension, you can choose to create it based on a table or a set of columns.
4. Select the measures you want to include in your cube.

4. Design Dimensions

Dimensions provide the analytical context. You can refine existing dimensions or create new ones.

Key aspects of dimension design:

• Attributes: Columns from your DSV that represent properties of the dimension (e.g., Product Name, Color, Size).
• Hierarchies: Organize attributes to allow for drill-down analysis (e.g., Year > Quarter > Month).
• Attribute Relationships: Define how attributes relate to each other within a dimension.
• User-defined Hierarchies: Create custom hierarchies beyond the natural key.

5. Design Measures

Measures are the numerical data points you want to analyze. You can define aggregation behaviors.

Common aggregation types:

• Sum: Adds up values.
• Count: Counts the number of occurrences.
• Average: Calculates the average.
• Min/Max: Finds the minimum or maximum value.

6. Deploy and Process the Model

Once your model is designed, you need to deploy it to an Analysis Services instance and process it to load the data.

1. Right-click on the Analysis Services project in Solution Explorer and select "Properties".
2. Configure the "Deployment Server" and "Database Name" for your SSAS instance.
3. Right-click on the project again and select "Deploy".
4. After successful deployment, right-click on the deployed database in Solution Explorer and select "Process". Choose the appropriate processing option (e.g., Full Process).

7. Query Your Cube

You can now connect to your deployed cube using various client tools like SQL Server Management Studio (SSMS), Excel, or custom applications using MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions).

Advanced Concepts

Once you are comfortable with the basics, you can explore more advanced features such as:

• Calculated members and measures
• Perspectives
• Mining structures
• Security roles
• Actions and KPIs

Next Steps

Now that you have a foundational understanding, we recommend exploring the following resources:

• MDX Query Language Reference
• DAX Guide for SSAS Tabular Models
• Tutorials for Creating Advanced SSAS Cubes