MOLAP

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.

MOLAP vs. ROLAP^[1]

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.

Trends

Many commercial OLAP tools now use a "Hybrid OLAP" (HOLAP) approach, which allows the model designer to decide which portion of the data will be stored in MOLAP.

Products

Examples of commercial products that use MOLAP are Cognos Powerplay, Oracle Database OLAP Option,ElegantJ BI, MicroStrategy, Microsoft Analysis Services, Essbase, TM1, Jedox, icCube. There is also MOLAP server Palo.

References

↑ Bach Pedersen, Torben; S. Jensen, Christian (December 2001). "Multidimensional Database Technology". Distributed Systems Online (IEEE): 40–46. ISSN 0018-9162.

Data warehouse

Creating the data warehouse

Concepts	Database Dimension Dimensional modeling Fact OLAP Star schema Aggregate

Variants	Anchor Modeling Column-oriented DBMS Data Vault Modeling HOLAP MOLAP ROLAP Operational data store

Elements	Data dictionary/Metadata Data mart Sixth normal form Surrogate key

Fact	Fact table Early-arriving fact Measure

Dimension	Dimension table Degenerate Slowly changing

Filling	Extract-Transform-Load (ETL) Extract Transform Load

Using the data warehouse

Concepts	Business intelligence Dashboard Data mining Decision support system (DSS) OLAP cube Data warehouse automation

Languages	Data Mining Extensions (DMX) MultiDimensional eXpressions (MDX) XML for Analysis (XMLA)

Tools	Business intelligence tools Reporting software Spreadsheet

People	Bill Inmon Ralph Kimball

Products	Comparison of OLAP Servers Data warehousing products and their producers

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