OLAP databases provide aggregated summary information quickly using a schema that is easily understood by end users. The OLAP design makes it unnecessary to "join" tables together as in a relational database, and includes all of the relationships required to enable a specific set of queries to be run against the database. The central object in an OLAP database is called a cube. A single OLAP database may contain many cubes; however, the team system database consists of a single cube.
You can use a client browsing tools such as Microsoft Excel or a 3rd party application to attach to the data cube. After you attach the client browser, you can drag and drop the cube elements onto a design surface or pivot table to formulate questions and retrieve answers quickly. The cube pre-aggregates information, and is optimized to answer questions like "What is the total number of bugs on each day that meet a set of criteria?"
The cube consists of two primary concepts: measures and dimensions. The measures are the (usually) numeric values that provide summaries at various different levels of aggregation. The dimensions are the way in which the numeric values are summarized. For example, Work Item Count is a measure that shows the total number of work items. When disaggregated by dimensions, such as Priority, Assigned To, Date, or State, you can use this measure to answer a wide range of questions.
Within the cube, measures are organized within measure groups. A measure group is associated with a single fact or event that is tracked by the OLAP database. Also, the measures can be summarized by various dimensions, some of which are common across the various measure groups. For example, Date, Build, Project, Person, Area, and Iteration are common across all of the measure groups.