Description
The relational database model uses a structure of attributes within tuples within relations to represent data (relations are erroneously referred to as tables in SQL-DBMSs). Tables can be linked by common key values. Edgar F. Codd first designed this model in 1970, while working for IBM, and its simplicity revolutionized database usage at the time. Codd's work was in many ways ahead of its time, as computing power could not support the overheads of his database system (Hasan 1999).
In the 1980s the power of computers had grown to the point where these overheads were no longer a problem, and today relational database management systems (RDBMS) are available on local desktops, as well as large organisational database management servers.

Why use dimensional databases?
Apart from the inherent advantages of using a multi-dimensional array structure, multi-dimensional databases also contain the following advantages.
Intuitive spreadsheet-like views of the data are the output of multi-dimensional databases. Such views are difficult to generate in relational systems without the use of complex SQL queries, while others cannot be performed by standard SQL at all, eg. top ten exam results.
Multi-dimensional databases are very easy to maintain, because data is stored in the same way as it is viewed, that is according to its fundamental attributes, so no additional computational overhead is required for queries of the database. Compare this to relational system, where complex indexing and joins may be used that require significant maintenance and overhead.
Multi-dimensional database achieve performance levels that are well in excess of that of relational systems performing similar data storage requirements. These high performance levels encourage and enable OLAP applications. Performance can be improved in relational systems through database tuning, but the database cannot be tuned for every possible on-the-fly query. In relational systems, tuning is quite specific, therefore decreasing flexibility, and also requires expensive database specialists.
In summary, multi-dimensional database systems are a complementary technology to entity relational systems, and in some circumstances it makes more sense to use multi-dimensional arrays rather than relational tables.
Where multi-dimensional systems excel over their relational system counterparts is in the area of data presentation and analysis, where the data in question leads itself to being suitable for multi-dimensional systems, such as where complex inter-relationships exist.
The top-level views of data over many combinations of dimensions make multi-dimensional systems particularly useful for trend analysis over time by management staff of organizations, due to te ease of viewing the data in a more naturally intuitive way.