| OLAP – Online Analytical Processing |
It is a technology, used to organize large business databases and supports to perform complex and multidimensional analysis, at high speeds on large volumes of historical data, aggregated from OLTP databases and other distinct sources. OLAP enables users to easily and selectively extract and query data in order to analyze. Common OLAP operations include slice and dice, drill down, drill up, roll up, and pivot.
- Slice: A slice is a subset of a multi-dimensional array corresponding to a single value for one or more members of the dimensions not in the subset.
- Dice: The dice operation is a slice on more than two dimensions of a data cube, or more than two consecutive slices.
- Drill Down/Up: Drilling down or up is a specific analytical technique whereby the user navigates among levels of data, ranging from the most summarized (up) to the most detailed (down).
- Roll-up: A roll-up involves computing all of the data relationships for one or more dimensions. To do this, define a computational relationship or formula.
- Pivot: A pivot changes the dimensional orientation of a report or page display.
| ROLAP – Relational Online Analytical Processing |
ROLAP supports OLAP by using techniques that implement multidimensionality in the two-dimensional tables of Relational Database Management Systems (RDBMS). Star schema joins are
a common database design technique used in ROLAP environments.
- ROLAP is used to handle the large amount of data
- ROLAP tools don’t use precalculated and prefabricated data cubes
- Data can be stored efficiently
- ROLAP can leverage functionalities inherent in the relational database
- Performance of ROLAP can be slow
- In ROLAP, difficult to maintain aggregate tables
- Limited by SQL functionalities
| MOLAP – Multidimensional Online Analytical Processing |
MOLAP supports OLAP by using proprietary and specialized multi-dimensional database technology.
- MOLAP is basically used for complex calculations
- MOLAP is optimal for operation such as slice and dice
- MOLAP allows fastest indexing to the precomputed / prefabricated summarized data
- MOLAP can’t handle large amount of data
- MOLAP, Requires additional investment
- Without re-aggregation, difficult to change dimension
| HOLAP – Hybrid Online Analytical Processing |
This is simply a combination of ROLAP and MOLAP. HOLAP implementations allow part of the data to be stored in MOLAP form and another part of the data to be stored in ROLAP. Implementations vary on the control a designer has to vary the mix of partitioning
- HOLAP provides the functionalities of both MOLAP and ROLAP
- HOLAP provides fast access at all levels of aggregation
- HOLAP architecture is very complex to understand because it supports both MOLAP and ROLAP
