Introduction to SSAS Cubes

What are SSAS Cubes?

SQL Server Analysis Services (SSAS) provides online analytical processing (OLAP) and data mining functionality for business intelligence applications. A core component of SSAS is the multidimensional model, often referred to as an "Analysis Services cube" or simply a "cube."

A cube is a multidimensional data structure that enables users to analyze large amounts of data from various perspectives. Unlike traditional relational databases that store data in tables, cubes store aggregated data in a way that optimizes for fast querying and analysis. This makes it ideal for business intelligence scenarios where users need to slice, dice, drill down, and roll up data to gain insights.

Key Concepts

Understanding the fundamental building blocks of an SSAS cube is crucial for its effective use and design.

Dimensions

Dimensions represent the business perspectives by which you want to analyze your data. They are analogous to the "who, what, where, when, why" of your business. Common examples include:

• Time: Year, Quarter, Month, Day
• Geography: Country, State, City
• Product: Category, Subcategory, Product Name
• Customer: Segment, Demographics, Individual Customer

Each dimension is typically composed of attributes, which are descriptive properties of the dimension.

Measures

Measures represent the numerical values that you want to analyze. These are typically the "facts" of your business, such as sales amount, quantity sold, profit, or cost. Measures are often aggregated using functions like SUM, COUNT, AVERAGE, MIN, or MAX.

For example, if you have a sales fact table, your measures might be:

-- Example Measures
SalesAmount (SUM)
QuantitySold (SUM)
AverageSellingPrice (AVERAGE)
NumberOfTransactions (COUNT)
            

Hierarchies

Hierarchies are special structures within dimensions that represent a parent-child relationship or a path of drill-down. They allow users to navigate from a high-level aggregation to more detailed levels. For instance, a 'Geography' dimension might have a hierarchy like:

World -> Continent -> Country -> State -> City

Similarly, a 'Time' dimension could have:

Year -> Quarter -> Month -> Day

KPIs (Key Performance Indicators)

KPIs are calculations that measure business performance against defined goals. SSAS allows you to define KPIs directly within the cube, making them easily accessible to end-users through BI tools. A KPI typically includes:

• A value (e.g., current sales)
• A goal (e.g., target sales)
• A status indicator (e.g., on track, needs attention, met)

Benefits of Using SSAS Cubes

Implementing SSAS cubes offers numerous advantages for organizations looking to leverage their data:

• Performance: Significantly faster query performance compared to direct querying of relational databases, especially for complex analytical queries.
• Ease of Use: Provides an intuitive, business-oriented view of data, making it easier for non-technical users to perform analysis.
• Consistency: Ensures that business logic and calculations are defined once and applied consistently across all reports and analyses.
• Ad-hoc Analysis: Empowers users to perform self-service BI and ad-hoc querying without relying heavily on IT departments.
• Advanced Analytics: Supports complex calculations, aggregations, and can integrate with data mining models for deeper insights.
• Scalability: Designed to handle large volumes of data and a growing number of users.

SSAS Architecture Overview

SSAS operates in two modes: Multidimensional and Tabular. This article focuses on the Multidimensional model.

The typical flow involves:

1. Data Source: Relational databases (SQL Server, Oracle, etc.), flat files, or other sources.
2. Data Source Views: A layer that defines how SSAS connects to and understands the data sources.
3. Cubes: The core multidimensional model, built from dimensions and measures derived from the data source views.
4. Client Applications: Tools like Excel, Power BI, SSRS, or custom applications that connect to the SSAS cubes to retrieve and visualize data.

SSAS uses the Multidimensional Expressions (MDX) query language for retrieving data from multidimensional cubes.

Getting Started

To start building SSAS cubes, you'll need:

• SQL Server Data Tools (SSDT) for Visual Studio, which includes Analysis Services projects.
• A relational database containing your fact and dimension data.

The development process typically involves:

1. Creating a new Analysis Services project in Visual Studio.
2. Defining data sources and data source views.
3. Creating dimensions based on your business entities.
4. Defining measures and measures groups.
5. Building and deploying the cube to an SSAS instance.
6. Connecting to the cube from a client tool for analysis.

Microsoft provides extensive documentation and tutorials to guide you through this process.

Conclusion

SSAS cubes are a powerful tool for transforming raw data into actionable business intelligence. By organizing data in a multidimensional structure, they enable rapid analysis, reporting, and decision-making across an organization. Understanding the core concepts of dimensions, measures, and hierarchies is the first step towards harnessing the full potential of SQL Server Analysis Services.