MOLAP: A Basic Overview In 2021
Introduction
MOLAP- Multidimensional OLAP uses the MOLAP multidimensional data cube for data analysis and is an OLAP category. The data is pre-aggregated and pre-computed, then re-summarized, and finally stored in a MOLAP and provides data views of multiple facets of the data. It works with a relational database. Rather than use multiple queries of multiple data tables, the MOLAP uses its stored arrays of multi-dimensional data and is hence much faster than the ROLAP or Relational Online Analytical Processing.
1) The architecture of MOLAP
The MOLAP architecture has 3 main components, namely the
- Database server
- MOLAP server
- Client/Front end devices
In this system, the interface is used at the user end with request reports. The logic layer of the application’s MDDB-Meta-Data Data Base retrieves data from the MOLAP database, which is then forwarded to the user/client front-end tool as a result. Since the architecture of the MOLAP is dependent on precompiled and pre-aggregated data, the MOLAP has limited capabilities when it dynamically calculates or creates data aggregations that are not stored or pre-calculated in it. For Ex: Generating a report of all customers with either a savings/ current account in a bank is a MOLAP example.
Comparing the MOLAP model to the ROLAP model, one needs quantifiably less storage than the ROLAP, where the techniques of compression of data are used and create several issues in the comparison of ROLAP vs MOLAP. The MOLAP data cubes are static or unchanged cubes created by data extraction from the operational databases and require that all data cubes be created before being used for data analysis. Another issue here is that ad-hoc queries cannot be used to create on-the-go data cubes. Thus MOLAPs works best with data that works on the pre-defined queries model and require a lot of detailing at the front-end server and its design.
2) Implementation of MOLAP
Here are the important MOLAP implementation considerations
- MOLAP tools use proprietary languages when querying though it provides click-and-drag tools also. Ex: Microsoft MDX.
- The strategy for cube building has to account for MOLAP’s storage and maintenance implications.
- It is slow in scaling if the dimension numbers are increased since it needs a larger number of cubes and sizes of cubes to do so.
- Its data structure can support data analyses of multiple subject areas where data needs both analysis and navigation. However, when the navigation changes, one has to reorganize the data structure physically.
- Cube probing needs API.
- The Database administrator tools for maintenance, building etc., of the database call for different tools and skills.
Key Points to be noted are that –
- Operations in MOLAP are called MOLAP processing.
- MOLAP tools can store data and thus remove designing complexities arising in a relational database storing the data for analysis.
- A MOLAP server has 2 implementation steps corresponding to its w storage levels when managing sparse and dense datasets meaning sparse storage is used if storage utilization is low.
- MOLAP facts are always stored as dimensions and multi-dimensional arrays for the user query process.
3) Advantages of MOLAP
MOLAP’s main advantage is that it can be used to analyze, manage, and store large volumes of multi-dimensional data. It has excellent features like caching, indexing, and optimized storage that support the Fast Query Performance. When comparing the difference between MOLAP and ROLAP relational databases, it requires considerably smaller data sizes. The automated MOLAP computation uses the higher levels of data aggregates helping in rapid analysis of less-defined and larger data. It is the best model for inexperienced users since MOLAP cubes are easier to use, have pre-aggregated and precompiled data arrays, and can handle slicing/dicing operations on multi-dimensional data once the cube is created and implemented.
4) Disadvantages of MOLAP
The MOLAP disadvantages are that it is not scalable in dynamic operations and can handle only small data quantities at a time generating solutions that are both large in volumes and lengthy. MOLAP storage can prove inefficient when using scattered data. MOLAP is less scalable compared to the ROLAP and is resource-intensive introducing data redundancy. When more than 10-dimensions are present, the MOLAP querying and updating features suffer. Also, MOLAPs cannot handle detailed data.
Conclusion
MOLAP or Multidimensional OLAP uses a multidimensional cube to facilitate analysis of the data analysis in the OLAP cube form. Its response time does not depend on the data summarizing levels, and it has 2 levels to manage multi-dimensional arrays of sparse and dense data. It helps analyze, manage and automate work with multidimensional data aggregates. However, it is less scalable than the ROLAP model and handles only limited data amounts. Small wonder then that business models and applications used by organizations with multiple data and multiple data sources can easily be represented by OLAP cubes. The MOLAP scores when dealing with predefined queries of the data, which is presented with the possibility of multiple viewpoints.
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