Relational On-Line Analytical Processing (ROLAP) performs dynamic multidimensional analysis of data stored in a relational database, rather than in a multidimensional database. The traditional OLAP's slice and dice functionality is equivalent to adding a WHERE clause in the SQL statement. The design may be structured in the form of a star or its variations. A typical use of ROLAP is for large data size that is infrequently queried, such as historical data.
The chart below highlights advantages and disadvantages of ROLAP.
| Advantages |
Well known environments (relational database). |
| Can leverage functionality that comes with relational database with ROLAP technologies. |
| Can be used with data warehouse and OLTP systems. |
| No pre-aggregation is needed - avoid the data explosion effect that some MOLAP implementations incur with large scale models. |
| Can handle large amounts of data - the limitation is the data size of the underlying relational database. OLAP itself has no limitation on data amount. |
| Full security and administration is provided through RDBMSs. |
| Performs better than MOLAP when the data is sparse. |
| Performance is getting better by adding more OLAP functions and employing various storage and query optimization techniques. |
| Disadvantages |
Performance can be slow, since each ROLAP report is a SQL query in the relational database. |
| Does not have complex functions that are provided by OLAP tools. |
| Limited by SQL functionality. |
| Hard to maintain aggregate tables in the data warehouse. |
| Major Players |
Discover 3 from Oracle, DSS Agent from MicroStrategy, MetaCube from IBM Informix, Platinum Beacon from Platinum, Brio, Business Objects, Cognos Powerplay |
