Logically OLAP servers present business users with multidimensional data from the data warehouse or data marts without concerned regarding how or where the data are stored.
However, physical architecture and implementation of OLAP server must consider data storage issues. A data warehouse server for OLAP processing includes following.
These are the intermediate servers that stand in between a relational backend server and client front-end tools. they use a relational or extended-relational DBMS.
To store and manage warehouse data and OLAP middle way to support missing values. ROLAP servers include optimization for a DBMS backend, implementation of aggregation navigation logic and additional tools and services. ROLAP technology tends to have greater scalability than MOLAP technology.
These servers support multidimensional views of data through array-based multidimensional storage engine. they map multidimensional views directly to data cube array structure.
The advantage of using data cube is that it allows fast indexing to pre-computed summarized data. with multidimensional data stores, the storage utilization may be low if the data set is sparse. In such cases, sparse matrix compression techniques should be explored.
Many MOLAP servers adapt a two level storage representation to handle dense and sparse data sets; Denser subcubes are identified and stored as the array structure whereas sparse subcubes employees compression technology for efficient storage utilization.
The hybrid HOLAP approach combines ROLAP and MOLAP technology, benefiting from the greater scalability of ROLAP and the faster computation of MOLAP.
For example, HOLAP server may allow the larger volume of detailed data to be stored in a relational database, while aggregations are kept in a separate MOLAP store. The Microsoft SQL server 2000 supports a hybrid OLAP server.