[2] OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining.
[5] OLAP tools enable users to analyse multidimensional data interactively from multiple perspectives.
OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing.
Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time.
[6]: 178 Even when data is manipulated it remains easy to access and continues to constitute a compact database format.
Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations – the operation known as processing.
Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache.
This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality.
In the OLAP industry ROLAP is usually perceived as being able to scale for large data volumes but suffering from slower query performance as opposed to MOLAP.
The OLAP Survey[usurped], the largest independent survey across all major OLAP products, being conducted for 6 years (2001 to 2006) have consistently found that companies using ROLAP report slower performance than those using MOLAP even when data volumes were taken into consideration.
Some companies select ROLAP because they intend to re-use existing relational database tables—these tables will frequently not be optimally designed for OLAP use.
The superior flexibility of ROLAP tools allows this less-than-optimal design to work, but performance suffers.
The undesirable trade-off between additional ETL cost and slow query performance has ensured that most 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 and which portion in ROLAP.
There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage.
In this mode HOLAP stores aggregations in MOLAP for fast query performance, and detailed data in ROLAP to optimize time of cube processing.
Moreover, we can store some dices in MOLAP and others in ROLAP, leveraging the fact that in a large cuboid, there will be dense and sparse subregions.
The first real standard API was OLE DB for OLAP specification from Microsoft which appeared in 1997 and introduced the MDX query language.
[27] The first product that performed OLAP queries was Express, which was released in 1970 (and acquired by Oracle in 1995 from Information Resources).
As a result, Codd's "twelve laws of online analytical processing" were explicit in their reference to Essbase.
OLAP clients include many spreadsheet programs like Excel, web application, SQL, dashboard tools, etc.
Many clients support interactive data exploration where users select dimensions and measures of interest.
An extensive list of clients appears in the visualization column of the comparison of OLAP servers table.