What is a cube?

Using the Cube Wizard in SQL Server 2005, you can efficiently define simple cubes by specifying metrics and dimensions. This tool helps you build a cube either from an existing data source or by generating a schema if no data source is used. In this tutorial, we'll focus on creating a cube based on an existing data source. For more details, refer to "Using Relational Architecture" and "Introducing the Schema Generation Wizard."

When working with an existing data source, the Cube Wizard connects to the database and reads data from the specified table to help define your metrics and dimensions. These elements are typically based on a fact table, a dimension table, or both. You can enable Auto-Generate to let the wizard automatically assign properties to columns in the dimension table and even create multi-level hierarchies. If Auto-Generate is disabled, you can manually create attributes and hierarchies within the Cube Wizard or later in the Cube Designer.

A cube is fundamentally composed of a fact table and multiple dimension tables. The fact table contains foreign keys linking to dimension tables and the actual metrics (like sales, quantity, etc.). Dimension tables hold key-value pairs, such as product IDs and names. Understanding a cube is like slicing a 3D object—each dimension represents a different angle of the fact data.

Dimensions can be shared across multiple cubes, for example, a single time dimension can be used in various cubes. A dimension table should remain clean and focused. For instance, instead of splitting time into hours, minutes, and seconds, it's better to separate it into year, month, and day for easier reporting and analysis.

By organizing time into distinct levels, you can analyze sales patterns during specific periods, such as AM vs. PM, which provides deeper insights into customer behavior.

In relational design, a subject domain represents a specific topic that contributes to the overall multidimensional database. Once the subject domain is defined, it’s best to use a model to map out the relationships between fact and dimension tables. This allows you to visualize the final structure early on, reducing the risk of changes during deployment.

Keep in mind that not all table structures follow traditional normalization rules. Some may need to be split into entities and relationships to better support the multidimensional model. Fact tables can also have duplicate records since they often contain multiple attributes from a single dimension.

Many fact tables in multidimensional databases are built using views, allowing for flexible and repeated data access through different join methods.

SQL Server Analysis Services (SSAS) is a powerful tool for building multidimensional databases. With the Business Intelligence Development Studio, you can create SSAS projects and start deploying cubes. It's important to organize your data views carefully, ensuring that only necessary tables and relationships are included in each cube.

Avoid putting all topics into a single cube, as this can reduce readability and maintainability. A multidimensional database can host multiple cubes, so distributing them appropriately is a good practice.

Besides symmetric dimensions, there are also asymmetric dimensions, which will be discussed in future sections.

SQL Server Report Services (SSRS) is a user-friendly tool for designing reports. It supports both relational and multidimensional data sources, making it ideal for business intelligence tasks. With SSRS, you can deploy reports to web pages and integrate them with other applications via web links.

Data sets in SSRS are based on measures and dimensions from a cube. Measures come from fact tables, while dimensions are derived from attributes in dimension tables. Hierarchies are built from these attributes to provide structured data navigation.

Consider the "Import" cube, which includes two metrics—“Package” and “Last”—and three dimensions: “Alignment,” “Source,” and “Time.” The members of these dimensions, such as “Terres” (from the “Direction” dimension), “Africa” (from the “Source” dimension), and “First Quarter” (from the “Time” dimension), represent specific values within the cube.

Metrics

The “Package” metric counts the number of imported packages, using the SUM function for aggregation. The “Last Time” metric tracks the most recent date received, using the MAX function.

Dimensions

The “Direction” dimension indicates how goods are transported, including options like land, air, sea, etc. The “Source” dimension shows where the goods originate, such as Africa or Asia. The “Time” dimension organizes data into quarters and halves of the year.

Aggregation

Analysis Services automatically aggregates values at higher levels of a hierarchy. For example, the “Time” dimension might show aggregated data for each quarter. Users can also combine multiple dimensions to get a comprehensive view of the data.

Once the cube is created, you can define aggregations to precompute values for faster query performance. For more details, see Aggregation and Aggregation Design in SSAS.

Mapping Metrics, Attributes, and Hierarchies

Metrics, attributes, and hierarchies in a cube are derived from columns in the fact and dimension tables. Each metric corresponds to a specific column in the fact table, while attributes map to dimension table columns to form hierarchical structures.

For example, the cell representing “Aviation,” “Africa,” and “First Quarter” in the cube is derived from multiple rows in the fact table. These rows share the same keys, indicating they belong to the same cube unit.

This example assumes a star schema with one fact table and multiple dimension tables. Another common structure is the snowflake schema, where some dimension tables are joined to other dimension tables rather than directly to the fact table.

If a cube has multiple fact tables, each fact table's metrics are grouped into measure groups, and each group is linked to specific dimensions through relationship definitions.

To edit a cube, you can use SQL Server Management Studio or Business Intelligence Development Studio. These tools offer a range of tabs in the Cube Designer, allowing you to manage dimensions, measures, and hierarchies effectively.

The Cube Wizard simplifies the process of creating cubes by guiding you through steps such as selecting data source views and defining metrics. You can add existing dimensions or create new ones. Alternatively, the Dimension Wizard can be used to build dimensions separately before integrating them into the cube.

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