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Getting Started with SQL Server Analysis Services Multidimensional

Welcome to this introductory guide to SQL Server Analysis Services (SSAS) in its multidimensional mode. This article is designed for developers and business intelligence professionals who are new to SSAS or looking to solidify their understanding of its core concepts.

What is SQL Server Analysis Services?

SQL Server Analysis Services is a component of SQL Server that provides Online Analytical Processing (OLAP) and data mining functionality for business intelligence applications. It enables users to analyze large amounts of data from various sources, creating reports and dashboards for better decision-making.

Multidimensional vs. Tabular Mode

SSAS offers two primary modes: Multidimensional and Tabular. This guide focuses on the Multidimensional mode, which is built around cubes, dimensions, and measures. While the Tabular mode offers a more modern, relational-like approach, the Multidimensional mode remains powerful and widely used for complex analytical scenarios.

Core Concepts of Multidimensional SSAS

1. Cubes

A cube is a data structure that allows for fast analysis of data. It is composed of dimensions and measures. Think of it as a multi-dimensional spreadsheet where you can slice and dice data along different axes.

2. Dimensions

Dimensions represent the perspectives or categories by which you want to analyze your data. Common examples include Time, Geography, Product, and Customer. Each dimension can have a hierarchy, allowing users to drill down or roll up data.

For example, a 'Time' dimension might have hierarchies for Year, Quarter, Month, and Day.

3. Measures

Measures are the quantitative data points you want to analyze, such as Sales Amount, Quantity Sold, or Profit. Measures are aggregated values within a cube.

Common aggregation functions for measures include Sum, Count, Average, Min, and Max.

4. Hierarchies

Hierarchies are logical structures within dimensions that allow for drill-down and roll-up analysis. They define parent-child relationships within a dimension's members.

5. Schemas (Star and Snowflake)

SSAS multidimensional models are typically built on top of data warehouses that use either a star schema or a snowflake schema.

• Star Schema: A central fact table surrounded by denormalized dimension tables. Simple and often performs well.
• Snowflake Schema: A central fact table with normalized dimension tables, where dimension tables can link to other dimension tables. More complex but can reduce redundancy.

Getting Started with Development

To start developing SSAS multidimensional models, you will need:

1. SQL Server installed with Analysis Services Multidimensional mode.
2. SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects.

Step-by-Step Process (High-Level)

1. Create a New Analysis Services Project: In Visual Studio/SSDT, create a new Analysis Services Multidimensional Project.
2. Create a Data Source: Define a connection to your data source (e.g., a SQL Server relational database).
3. Create a Data Source View: A logical view of the data from your data sources. You can define relationships and perform light transformations here.
4. Create Dimensions: Based on your dimension tables from the data source view.
5. Create a Cube: Select your fact table and desired measures, and link it to your created dimensions.
6. Process the Cube: Load the data from your data source into the SSAS cube.
7. Deploy the Cube: Make the cube accessible for querying.
8. Query the Cube: Use tools like Excel, Power BI, or MDX queries to analyze the data.

Next Steps

This article provides a foundational understanding. For deeper learning, explore:

• Multidimensional Expression (MDX) language for querying cubes.
• Advanced dimension and cube design techniques.
• Performance tuning and optimization strategies.
• Security models within SSAS.

Continue your journey by exploring more advanced topics and practical examples. Happy analyzing!