Types of OLAP Systems
OLAP systems vary quite a lot, and they have generally been distinguished by a letter tagged onto the front of the word OLAP. ROLAP and MOLAP are the big players, and the other distinctions represent little more than the marketing programs on the part of the vendors to distinguish themselves, for example, SOLAP and DOLAP. Here, we aim to give you a hint as to what these distinctions mean.
Relational OLAP (ROLAP) –Star Schema based
Considered the fastest growing OLAP technology style, ROLAP or “Relational” OLAP systems work primarily from the data that resides in a relational database, where the base data and dimension tables are stored as relational tables. This model permits multidimensional analysis of data as this enables users to perform a function equivalent to that of the traditional OLAP slicing and dicing feature. This is achieved thorough use of any SQL reporting tool to extract or ‘query’ data directly from the data warehouse. Wherein specifying a ‘Where clause’ equals performing a certain slice and dice action.
One advantage of ROLAP over the other styles of OLAP analytic tools is that it is deemed to be more scalable in handling huge amounts of data. ROLAP sits on top of relational databases therefore enabling it to leverage several functionalities that a relational database is capable of. Another gain of a ROLAP tool is that it is efficient in managing both numeric and textual data. It also permits users to “drill down” to the leaf details or the lowest level of a hierarchy structure.
However, ROLAP applications display a slower performance as compared to other style of OLAP tools since, oftentimes, calculations are performed inside the server. Another demerit of a ROLAP tool is that as it is dependent on use of SQL for data manipulation, it may not be ideal for performance of some calculations that are not easily translatable into an SQL query.
Multidimensional OLAP (MOLAP) –Cube based
Multidimensional OLAP, with a popular acronym of MOLAP, is widely regarded as the classic form of OLAP. One of the major distinctions of MOLAP against a ROLAP tool is that data are pre-summarized and are stored in an optimized format in a multidimensional cube, instead of in a relational database. In this type of model, data are structured into proprietary formats in accordance with a client’s reporting requirements with the calculations pre-generated on the cubes.
This is probably by far, the best OLAP tool to use in making analysis reports since this enables users to easily reorganize or rotate the cube structure to view different aspects of data. This is done by way of slicing and dicing. MOLAP analytic tool are also capable of performing complex calculations. Since calculations are predefined upon cube creation, this results in the faster return of computed data. MOLAP systems also provide users the ability to quickly write back data into a data set. Moreover, in comparison to ROLAP, MOLAP is considerably less heavy on hardware due to compression techniques. In a nutshell, MOLAP is more optimized for fast query performance and retrieval of summarized information.
There are certain limitations to implementation of a MOLAP system, one primary weakness of which is that MOLAP tool is less scalable than a ROLAP tool as the former is capable of handling only a limited amount of data. The MOLAP approach also introduces data redundancy. There are also certain MOLAP products that encounter difficulty in updating models with dimensions with very high cardinality.
Hybrid OLAP (HOLAP)
HOLAP is the product of the attempt to incorporate the best features of MOLAP and ROLAP into a single architecture. This tool tried to bridge the technology gap of both products by enabling access or use to both multidimensional database (MDDB) and Relational Database Management System (RDBMS) data stores. HOLAP systems stores larger quantities of detailed data in the relational tables while the aggregations are stored in the pre-calculated cubes. HOLAP also has the capacity to “drill through” from the cube down to the relational tables for delineated data.
Some of the advantages of this system are better scalability, quick data processing and flexibility in accessing of data sources.
There are also less popular types of OLAP styles upon which one could stumble upon every so often. We have listed some of the less famous types existing in the OLAP industry.
Web OLAP (WOLAP)
Simply put, a Web OLAP which is likewise referred to as Web-enabled OLAP, pertains to OLAP application which is accessible via the web browser. Unlike traditional client/server OLAP applications, WOLAP is considered to have a three-tiered architecture which consists of three components: a client, a middleware and a database server.
Probably some of the most appealing features of this style of OLAP are the considerably lower investment involved, enhanced accessibility as a user only needs an internet connection and a web browser to connect to the data and ease in installation, configuration and deployment process.
But despite all of its unique features, it could still not compare to a conventional client/server machine. Currently, it is inferior in comparison to OLAP applications which involve deployment in client machines in terms of functionality, visual appeal and performance.
Desktop OLAP (DOLAP)
Desktop OLAP, or “DOLAP” is based on the idea that a user can download a section of the data from the database or source, and work with that dataset locally, or on their desktop. DOLAP is easier to deploy and has a cheaper cost but comes with a very limited functionality in comparison with other OLAP applications.
Mobile OLAP (MOLAP)
Mobile OLAP is merely refers to OLAP functionalities on a wireless or mobile device. This enables users to access and work on OLAP data and applications remotely thorough the use of their mobile devices.
Spatial OLAP (SOLAP)
With the aim of integrating the capabilities of both Geographic Information Systems (GIS) and OLAP into a single user interface, “SOLAP” or Spatial OLAP emerged. SOLAP is created to facilitate management of both spatial and non-spatial data, as data could come not only in an alphanumeric form, but also in images and vectors. This technology provides easy and quick exploration of data that resides on a spatial database.
Other different blends of an OLAP product like the less popular ‘DOLAP’ and ‘ROLAP’ which stands for Database OLAP and Remote OLAP, ‘LOLAP’ for Local OLAP and ‘RTOLAP’ for Real-Time OLAP are existing but have barely made a noise on the OLAP industry.