What are the benefits and challenges of using multidimensional OLAP (MOLAP) versus relational OLAP (ROLAP)?

2 What is ROLAP?

ROLAP is a method of storing OLAP data in a relational database, such as a table or a view. A relational database organizes data into rows and columns, where each row represents a record and each column represents a field. A table can have one or more primary keys that uniquely identify each record, and one or more foreign keys that link to other tables. A view is a virtual table that combines data from one or more tables based on a query. ROLAP uses SQL (structured query language) to access and manipulate data from the relational database.

3 Benefits of MOLAP

MOLAP has some advantages over ROLAP for OLAP data modeling and analysis, such as faster performance, easier analysis, and better security. MOLAP pre-computes and stores the aggregated data in the cube, which reduces query time and improves response time. It also uses specialized indexing and compression techniques to optimize data storage and retrieval. Furthermore, MOLAP provides a user-friendly interface that allows users to explore and slice-and-dice the data along different dimensions and levels. Additionally, it supports complex calculations and functions that can be applied to the measures, as well as access rights and permissions for different dimensions, levels, and measures. This helps protect data integrity and consistency by preventing unauthorized changes or updates.

4 Challenges of MOLAP

MOLAP has some drawbacks and limitations that need to be taken into account. This includes higher cost due to the need for more hardware and software resources, as well as more disk space and memory for storing pre-aggregated data and indexes. Additionally, MOLAP’s predefined structure and schema may not be able to accommodate dynamic or changing user needs, nor can it handle sparse or missing data. Lastly, MOLAP requires periodic refreshes and rebuilds of the cube, which can take a long time and affect the availability and freshness of the data.

5 Benefits of ROLAP

ROLAP has several advantages over MOLAP for OLAP data modeling and analysis. For instance, it is more cost-effective because it uses the existing relational database infrastructure and tools, saving disk space and memory by storing only the raw data. Additionally, ROLAP offers greater flexibility as it can handle any type and size of data, as well as cope with sparse or missing data, filling in the gaps or ignoring null values. Furthermore, ROLAP can access and update the data in real time without having to refresh or rebuild the cube, leading to shorter update times and improved efficiency and accuracy of the data.

6 Challenges of ROLAP

ROLAP has some drawbacks and limitations that need to be taken into account. For example, it can lead to slower performance due to computing the aggregated data on the fly, as well as relying on the performance and optimization of the relational database and SQL query engine. Furthermore, ROLAP requires more technical skills and knowledge to write and execute SQL queries, and it has limited support for complex calculations and functions. In addition, ROLAP inherits the access rights and permissions from the relational database, which may not be sufficient or appropriate for different dimensions, levels, and measures. Moreover, ROLAP can expose the data to potential errors or inconsistencies due to concurrent or conflicting updates or changes.