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MOLAP (multidimensional online analytical processing) is an alternative to the ROLAP (relational OLAP) technology. While both ROLAP and MOLAP analytic tools are designed to allow analysis of data through the use of a multidimensional data model, MOLAP differs significantly in that (in some software) it requires the pre-computation and storage of information in the cube—the operation known as processing. Most MOLAP solutions store these data in an optimized multidimensional array storage, rather than in a relational database (i.e. in ROLAP).
There are many methodologies and algorithms for efficient data storage, aggregation and implementation specific business logic with a MOLAP Solution. As a result, there are many misconceptions to what the term specifically implies.
Advantages of MOLAP
- Fast query performance due to optimized storage, multidimensional indexing and caching.
- Smaller on-disk size of data compared to data stored in relational database due to compression techniques.
- Automated computation of higher level aggregates of the data.
- It is very compact for low dimension data sets.
- Array models provide natural indexing.
- Effective data extraction achieved through the pre-structuring of aggregated data.
Disadvantages of MOLAP
- Within some MOLAP Solutions the processing step (data load) can be quite lengthy, especially on large data volumes. This is usually remedied by doing only incremental processing, i.e., processing only the data which have changed (usually new data) instead of reprocessing the entire data set.
- Some MOLAP methodologies introduce data redundancy.
Examples of commercial products that use MOLAP are Cognos Powerplay, Oracle Database OLAP Option,ElegantJ BI, MicroStrategy, Microsoft Analysis Services, Essbase, TM1, Jedox, icCube and Kylin. There is also MOLAP server Palo.