## Explain the storage models of OLAP

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How are you? My name is Rizwan. There is question in my mind and I want to get its solution because, I tried my best, but yet not found its answer. Anybody please help me in this case. Kindly explain the storage models of OLAP. My friend sent me its answer, but I am not satisfied from his answer. So please solve my problem as soon as possible.

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Answered By 40 points N/A #88945

## Explain the storage models of OLAP

Hi dear its me M.Smith and I am a Developer and web designer.

So, don’t worry its so easy question and it’s my wish to help you. MOLAP (Multidimensional Online Analytical processing) In MOLAP data is stored in form of multidimensional cubes and not in relational databases. Advantage Excellent query performance as the cubes have all calculations pre-generated during creation of the cube. Disadvantages It can handle only a limited amount of data.

Since all calculations have been pre-generated, the cube cannot be created from a large amount of data. It requires huge investment as cube technology is proprietary and the knowledge base may not exist in the organization. ROLAP (Relational Online Analytical processing). The data is stored in relational databases.

Advantages It can handle a large amount of data and It provides all the functionalities of the relational database. Disadvantages It is slow. The limitations of the SQL apply to the ROLAP too. HOLAP (Hybrid Online Analytical processing). HOLAP is a combination of the above two models. It combines the advantages in the following manner: For summarized information it makes use of the cube. For drill down operations, it uses ROLAP.

Answered By 0 points N/A #88946

## Explain the storage models of OLAP

This is the storage models of OLAP.

MOLAP Multidimensional Online Analytical processing:

• In MOLAP data is stored in form of multidimensional cubes and not in relational databases. The calculations are pre-generated at the time of creating cube and easily applied while querying data.

• Excellent query performance as the cubes have all calculations pre-generated during creation of the cube.

• It can handle only a limited amount of data. Since all calculations have been pre-generated, the cube cannot be created from a large amount of data.

ROLAP Relational Online Analytical processing:

• The data is stored in relational databases.
•  The data is stored in relational databases and provides an appearance of traditional OLAP’s slicing and dicing functionality.

• It can handle a large amount of data and it provides all the functionalities of the relational database.

• It is slow.
• The limitations of the SQL apply to the ROLAP too.
• HOLAP Hybrid Online Analytical processing.

HOLAP is a combination of the above two models. It combines the advantages in the following manner:

• For summarized information it makes use of the cube.
• For drill down operations, it uses ROLAP.
Answered By 0 points N/A #88948

## Explain the storage models of OLAP

OLAP is a data model in which data is viewed as cubes with dimension and measures. Running complex queries, slicing data into subsets etc critical tasks are made easy in the multidimensional data model.

In each dimension, you can organize the data into a hierarchy of levels of detailing. A particular instance of the OLAP data model would have the specific values for each level in the hierarchy. A user viewing OLAP data can move up or down between levels to view information that is either more or less detailed.

This model allows users to browse complex data intuitively. A key tenet of OLAP is that users should see consistent response times for each view, or slice, of the data they request. Because data is usually collected at the detail level only, the information summary usually is computed in advance. These precomputed values, or aggregations, are the basis of the OLAP performance gains.

In the early days of OLAP technology, most vendors assumed that the only possible solution for OLAP applications was a specialized, nonrelational storage model. Later, other vendors discovered that through the use of database structures (star and snowflake schemas), indexing, and storage of aggregates, relational database management systems (RDBMS) could be used for OLAP. These vendors called their technology Relational OLAP (ROLAP). The earlier OLAP vendors then adopted the term multidimensional OLAP (MOLAP).

MOLAP implementations usually outperform ROLAP technology, but have problems with scalability. On the other hand, ROLAP implementations are more scalable and are often attractive to customers because they leverage investments in existing relational database technology.

A recent development has been a hybrid OLAP (HOLAP) solution, which combines the ROLAP and MOLAP architectures to yield a solution with the best features of both: superior performance and extensive scalability. One approach to HOLAP maintains detailed records (the largest volumes) in the relational database, while maintaining aggregations in a separate, MOLAP store