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Najeeb Momoh
Najeeb Momoh

Multidimensional Vs Multi Relational Olap Pdf 73

Multidimensional vs Multi Relational OLAP

OLAP (Online Analytical Processing) is a software technology that enables fast and flexible analysis of large amounts of data from a data warehouse or other centralized data store. OLAP can be classified into two main types: multidimensional OLAP (MOLAP) and multi relational OLAP (ROLAP). These two types differ in how they store and access the data, as well as their advantages and disadvantages. In this article, we will compare and contrast MOLAP and ROLAP, and discuss when to use each one.

What is MOLAP?

MOLAP is a type of OLAP that stores data in a multidimensional array, also known as an OLAP cube. An OLAP cube is a data structure that organizes data into multiple dimensions, each representing a category or hierarchy of the data. For example, sales data can be arranged into dimensions such as time, product, region, customer, etc. Each dimension can have multiple levels of detail, such as year, quarter, month, week, day for the time dimension. Each data point in the cube is located at the intersection of multiple dimensions, such as sales amount for product A in region B on day C.

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MOLAP extracts data from relational tables and transforms it into a multidimensional format, which enables faster and more efficient processing and analysis. MOLAP can perform complex calculations and aggregations on the data before storing it in the cube, which reduces the query time and improves performance. MOLAP also supports advanced features such as drill-down, roll-up, slice-and-dice, and pivot, which allow users to explore and manipulate the data from different perspectives and levels of detail.

What is ROLAP?

ROLAP is a type of OLAP that stores data in relational tables, using the same format as the source data. ROLAP does not create a separate multidimensional structure for the data, but rather uses SQL queries to access and analyze the data directly from the relational database. ROLAP can leverage the existing features and capabilities of the relational database management system (RDBMS), such as indexing, partitioning, security, etc.

ROLAP does not require any data transformation or pre-computation, which saves storage space and maintenance cost. ROLAP can handle large volumes of data and complex queries that involve multiple tables and joins. ROLAP also supports dynamic and ad-hoc analysis, as users can create new dimensions and measures on the fly without modifying the underlying data structure.

MOLAP vs ROLAP: Comparison

The following table summarizes some of the main differences between MOLAP and ROLAP:




Data storage

Multidimensional array (OLAP cube)

Relational tables

Data access

Cube operations

SQL queries

Data processing

Pre-computed and stored in the cube

Computed on demand from the database


Fast for predefined queries and calculations

Slow for complex queries and calculations


Limited by cube size and complexity

High for large volumes of data


Low for dynamic and ad-hoc analysis

High for dynamic and ad-hoc analysis

MOLAP vs ROLAP: When to use?

The choice between MOLAP and ROLAP depends on various factors, such as the size and complexity of the data, the type and frequency of the queries, the performance requirements, the available resources, etc. Generally speaking, MOLAP is more suitable for scenarios where:

  • The data volume is moderate and the data structure is stable and well-defined

  • The queries and calculations are predictable and predefined

  • The performance and response time are critical and need to be optimized

  • The storage space and maintenance cost are not a major concern

On the other hand, ROLAP is more suitable for scenarios where:

  • The data volume is large and the data structure is dynamic and flexible

  • The queries and calculations are complex and ad-hoc

  • The performance and response time are acceptable and can be compromised

  • The storage space and maintenance cost are a major concern


MOLAP and ROLAP are two types of OLAP that differ in how they store and access the data for analysis. MOLAP uses a multidimensional array (OLAP cube) to store pre-computed and aggregated data, which enables fast and efficient processing. ROLAP uses relational tables to store raw data, which allows for dynamic and flexible analysis. Both types have their advantages and disadvantages, and the choice between them depends on the specific needs and preferences of the users and the applications.

This article is based on information from the following sources:

  • [What is OLAP? IBM]

  • [Exploring the Differences: Multi-dimensional Modeling vs Relational Modeling in Analytical Systems Advancing Analytics]

  • [Difference between ROLAP and MOLAP - GeeksforGeeks]


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