Ranet OLAP

Overview

There are basically three types of online analytical processing: MOLAP, ROLAP, and HOLAP and in this article we’re going to speak about MOLAP particularly. MOLAP is short for multidimensional online analytical processing and is taken for the traditional version of OLAP. It’s typically noted for its competitive speed and high user-responsiveness. Its core attribute constitutes data maintenance in a multidimensional structure which is commonly referred to as a cube. The cube is populated with data from the operational databases (CRM, ERP, AP etc.) or from the data warehouse. The cube includes a fact table in the center and multiple dimensions with the required information. If the cube has more than three dimensions it is taken for a hypercube. Before data processing and analyzing MOLAP tools require precalculating data aggregations to build dimensions for the cube. Due to the aggregations, MDX queries are run rapidly and it’s one of the main MOLAP’s advantages. Nevertheless, the very process of cube build for multidimensional online analytical processing may take up a lot of time depending on the quantity of aggregations needed to be computed.

olap

Comparison with ROLAP and HOLAP

As compared with MOLAP, ROLAP, as the name implies, operates directly with relational databases. Fact data and dimension tables are stored in relational tables. In contrast to MOLAP tools, ROLAP tools don’t comprise data precomputation and that is the reason why the performance isn’t fast as the queries are composed towards relational tables.

In hybrid OLAP - HOLAP, some aggregations are stored in a multidimensional structure as well as in MOLAP, though sometimes the database exploits relational tables to maintain bigger detailed data quantities. When there’s a need to extract requisite information from HOLAP the system calls the database through information from the fact table, as well as in ROLAP.

MOLAP benefits

  • MOLAP is famous for efficient and fast performance owing to streamlined indexing and optimizations in the storage. Data is rapidly extracted, can be sliced and diced to provide different views of the information.
  • Multidimensional OLAP provides all potential data combinations displayed in the multidimensional array and an opportunity to directly access these combinations.
  • Multidimensional online analytical processing system requires less storage space in comparison with ROLAP due to compression techniques.
  • MOLAP is easily deployed and compact thereby suits for low dimension datasets.

MOLAP drawbacks

  • MOLAP cubes can’t be changed and have to be developed before being exploited.
  • The amount of data which the system can manage is limited.
  • MOLAP technology is proprietary and requires both human and financial resources investment.
  • The type of OLAP described in the article provides users with in-depth multidimensional data analysis, trends awareness and predictions for successful business development.



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