The Cube Wizard in SQL Server 2005 is a powerful tool that simplifies the process of defining cubes. It allows users to easily set up metrics and dimensions for their data models. The wizard can either connect to an existing data source or generate its own schema if no data source is provided. In this tutorial, we will focus on creating a cube based on an existing data source. For more detailed information, refer to the guides on relational architecture and the Schema Generation Wizard.
When you create a cube using an existing data source, the wizard connects to the database specified in the data source object and reads the data from the designated table to help define the metrics and dimensions. These elements are typically derived from fact tables, dimension tables, or both. By enabling Auto-Generate, the Cube Wizard can automatically determine column properties in dimension tables and even attempt to build multi-level hierarchies. If Auto-Generate is disabled, you can manually define attributes and hierarchies within the Cube Wizard or do so later in Cube Designer.
Cube
At its core, a cube consists of a fact table and multiple dimension tables. The fact table contains foreign keys linking to dimensions and the actual metrics. Dimension tables, on the other hand, hold key-value pairs such as ID and Name. A simple way to understand a cube is to think of it as a three-dimensional structure where the fact table is the center, and dimension tables wrap around it to form different perspectives.
Dimensions can be shared across multiple cubes. For example, a time dimension is often used consistently across various cubes. It's important to keep dimension tables clean and focused. For instance, a time dimension should not include minute-by-minute details unless necessary. Instead, breaking it down into years, months, and days makes it more efficient and easier to manage for reporting purposes.
By separating time into distinct levels like year, quarter, and day, you gain more flexibility when analyzing sales trends. This approach also helps in understanding customer behavior over specific time periods without overcomplicating the model.
Relational Table Design
A subject domain represents a specific business area and forms part of a multidimensional database. After identifying the subject domain, it's best to use a model to define primary and foreign key relationships. This visual representation helps you quickly see the final structure of your fact and dimension tables, reducing the risk of changes during deployment.
It’s important to note that some table structures may not follow traditional normalization rules. These non-normalized structures should be broken down into smaller entities and relationships to improve clarity and maintainability. Fact tables can also contain duplicate records, especially when a single dimension has multiple attributes, making direct design into a single table less effective.
Many fact tables in multidimensional databases are built on top of views. Views allow for flexible data aggregation and repeated access through different join methods, making them a valuable tool in complex data environments.
SSAS
SQL Server Analysis Services (SSAS) is a Microsoft technology used to build multidimensional databases. With the Business Intelligence components in Visual Studio, you can create SSAS projects and start deploying analytical solutions.
When designing data views, ensure that all necessary tables and views are included and that relationships are properly defined. Avoid placing all subject areas into a single cube, as this can lead to poor readability and maintenance. Remember, a multidimensional database can host multiple cubes, so distributing them appropriately improves performance and usability.
In addition to symmetric dimensions, there are also asymmetric dimensions, which will be discussed in future sections.
SSRS
SQL Server Report Services (SSRS) is a visualization tool from Microsoft that supports both relational and multidimensional databases. Using the extensions introduced earlier, you can create SSRS projects and deploy them to web pages, enabling seamless integration with other systems.
Data sets in SSRS are defined based on the metrics and dimensions of the cube. These metrics and dimensions are derived from the tables and views in the data source view. A cube typically includes one or more fact tables for metrics and one or more dimension tables for attributes, which are then organized into hierarchies.
Cube Example
Consider the "Import" cube, which includes two metrics—“Package†and “Lastâ€â€”and three dimensions: Alignment, Source, and Time. Smaller alphanumeric values represent members of these dimensions, such as "Terres" (from the "Direction" dimension), "Africa" (from the "Source" dimension), and "First Quarter" (from the "Time" dimension).
Metrics
The values in the cube represent two metrics: “Package†and “Last.†The “Package†metric counts the number of imported packages, using the Sum function for aggregation. The “Last†metric represents the date received, using the Max function for aggregation.
Dimensions
The “Direction†dimension indicates how goods are transported, including options like land, air, sea, etc. The “Source†dimension shows the origin of the imports, such as Africa or Asia. The “Time†dimension breaks the year into quarters and halves.
Aggregation
Business users can analyze metrics across different levels of each dimension. For example, the “Last†metric can be aggregated within the calendar hierarchy of the Time dimension. Metrics can also combine members from multiple dimensions, allowing for complex analyses, such as examining imports by region and transport method across different quarters.
Once the cube is created, you can define aggregations to precompute certain ranges of data, improving query performance. For more information, see Aggregation and Aggregation Design in SSAS.
Mapping Metrics, Attributes, and Hierarchies
Metrics, attributes, and hierarchies in a cube are mapped from columns in the fact and dimension tables. Each cube unit typically comes from multiple rows in the fact table, with matching keys indicating the same cube cell.
The example shown works for simple star schemas. However, snowflake schemas, where dimension tables are joined to other dimensions, are also common. For more details, see Dimensions in SSAS.
If a cube has multiple fact tables, each fact table is grouped into a measure group, and each group is associated with specific dimensions through defined relationships. These relationships determine the granularity of the data and how dimensions interact with facts.
To edit a cube, you can use tools like SQL Server Management Studio or Business Intelligence Development Studio. Both provide a range of tabs in the Cube Designer, each serving a specific purpose in cube development and configuration.
The Cube Wizard is an efficient way to create cubes quickly. It guides you through selecting data sources, defining measures, and setting up dimensions. You can add existing dimensions or create new ones, or use the Dimension Wizard separately before integrating it into the cube. For more details, see Creating Dimensions.
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