Updated July 6, 2023
Difference Between ROLAP vs MOLAP vs HOLAP
ROLAP vs MOLAP vs HOLAP are the associated terminologies for data warehousing that represents logical data models. ROLAP means relational online analytical processing for relational data. MOLAP is known as multidimensional online analytical processing those implements through multiple data dimensions. HOLAP is known as hybrid online analytical processing that works for both ROLAP and MOLAP concepts. The data storage and data arrangements, designed view access in the data warehouse varies depending upon the type of the OLAP implementation. ROLAP SQL is being the querying technique, whereas MOLAP works with the sparse matrix, and HOLAP uses both SQL and sparse matrix technologies.
Head to Head Comparison Between ROLAP and MOLAP and HOLAP (Infographics)
Below is the top 8 comparison between ROLAP vs MOLAP vs HOLAP:
Key Differences Between ROLAP vs MOLAP vs HOLAP
Let us discuss some of the major key differences between ROLAP and MOLAP and HOLAP:
- ROLAP is relational OLAP where the data is arranged in traditional methods like rows and columns in the data warehouse. It is visible and accessible to users in multi-dimensional form. To display it as a multi-dimensional view the data is designed as the related layer of metadata which supports the collection and storage of data. It does dynamically in handling the complex query. It is slower than MOLAP where ROLAP deals with the enormous volume of data at a higher speed.
- MOLAP is a multi-dimensional OLAP where the data is analyzed on the registered system. The data is arranged in a multi-dimensional array. The array carries predefined data when the data is loaded in database management. MOLAP system is implemented on the application layer and when the user sends any request it fetches the data with the minimum response time.
- The expressing power of the relational model does not include the topics of dimension and measure to create a specific data type. The basic elements include integrity, attributes, relations which are mainly applied in Star schema.
- ROLAP uses SQL as its functioning language to fetch the data and work on it, whereas the MOLAP uses the Sparse matrix technique to get the data from multi-dimensional array in the form of dimensional data cubes.
- ROLAP has slow response time because it shows the multi-dimensional form of any data but MOLAP is very fast since it does not show any multi-dimensional view.
- Both ROLAP and MOLAP handle complex query and it has its unique performance. If the user wants any fast response system he can adopt to MOLAP
- ROLAP and MOLAP work on optimization techniques and created due to its sparsity.
- Here the intermediate structure HOLAP formed with a mixture of advantages of MOLAP and ROLAP. A large amount of data handling capacity is taken from ROLAP and the query speed method is taken from MOLAP which is fed to HOLAP which stands as a standardized model. HOLAP relies on its enormous data should be saved in a relational database management system to get rid of flaws created by sparsity and multi-dimensional engine which stores only the required information of the user and provide them frequent access. But if the user request more related data to solve any complex query it provides transparent access to that portion of a relational database. This HOLAP technique is adopted by popular MicroStrategy to increase their platform performance in partnership with other vendors who have already implemented this solution in their business.
- But in this design, there are few troubles which should be overcome to have a high performance.
- The quality of the process should be enhanced to satisfy client requirements. The quality should be consistent in data warehousing from the initial phase to the end phase. The few main areas where quality should be considered are defining areas, measuring areas and maximizing parts.
- The important qualities are accuracy, updated data, completed data, consistency, traceability, availability, and clarity.
- In Accuracy, the data should have the correct and real values because at the time of ETL the chances of missing values are high and also giving nonstandard value to any attribute should be avoided
- The data should be updated periodically and should not contain any old data
- The data cubes should not be missed. Because each data set represent unique primary keys and all the values should be stored from top to bottom and should be available as a complete data
- The representation of data should be in a proper arrangement in an orderly manner where it gives the user a high consistency performance.
- The data should be easily available and accessible to the user at any time
- The data pool should have the correct navigation about the sources so that the user can easily direct to that part of data without any wastage of time
- The data should have high clarity and should be easy to understand.
Comparison Table of ROLAP vs MOLAP vs HOLAP
The table below summarizes the comparisons between ROLAP vs MOLAP vs HOLAP:
| Basics for comparison | ROLAP | MOLAP | HOLAP |
| Acronym | Relational online analytical processing | Multi-dimensional online analytical processing | Hybrid online analytical processing |
| Storage methods | Data is stored on the main data warehouse | Data is stored on the registered database MDDB | Data is stored on the relational databases |
| Fetching methods | Data is fetched from the main repository | Data is fetched from the Proprietary database | Data is fetched from the relational databases |
| Data Arrangement | Data is arranged and saved in the form of tables with rows and columns | Data is arranged and stored in the form of data cubes | Data is arranged in multi-dimensional form |
| Volume | Enormous data is processed | Limited data which is kept in proprietary is processed | Large data can be processed |
| Technique | It works with SQL | It works with Sparse Matrix technology | It uses both Sparse matrix technology and SQL |
| Designed view | It has dynamic access | It has a static access | It has dynamic access |
| Response time | It has Maximum response time | It has Minimum response time | It takes Minimum response time |
Conclusion
The main topic should be discussed here is Information Security which should be carried from the development stage to the implementation stage and it is performed on its maintenance time also. Security is a key element for data warehousing because that is a place where the solution to crucial problems is taken and a large amount of data transaction and processing is done. The management and its auditing systems are crucial for data warehousing as important as the security system. The enterprise takes advantage of this online analytical processing system and implies it according to the demand.
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