What is MOLAP (Multidimensional OLAP) in Data Warehouse?

What is MOLAP?

Multidimensional OLAP (MOLAP) is a classical OLAP that facilitates data analysis by using a multidimensional data cube. Data is pre-computed, re-summarized, and stored in a MOLAP system (a major difference from ROLAP). Using a MOLAP engine, a user can explore multidimensional view data with different facets.

Multidimensional data analysis is also possible if a relational database is used, but that approach would require querying data from multiple tables. On the contrary, MOLAP has all possible combinations of data already stored in a multidimensional array. MOLAP can access this data directly. Hence, MOLAP is faster compared to Relational Online Analytical Processing (ROLAP).

Why Use MOLAP?

Organizations choose MOLAP when query speed, predictable response time, and ease of use matter more than raw scalability. Because data is pre-aggregated, analysts get instant feedback while slicing and dicing across hierarchies such as time, region, and product.

• Consistent, sub-second response times across summary levels.
• Optimized storage, indexing, and caching for analytical workloads.
• Intuitive cube model that mirrors how business users think about data.
• Native support for complex calculations and time-series analysis.

How MOLAP Works

MOLAP loads data from source systems, computes aggregations across every dimension combination, and stores the results in a compact multidimensional structure. When a user issues a query, the MOLAP server reads precomputed values directly, skipping costly joins or runtime aggregation.

This pre-computation step is what gives MOLAP its speed advantage. The trade-off is a longer cube build process and higher storage cost when dimensions become very wide or very sparse.

MOLAP Architecture

MOLAP Architecture includes the following components:

• Database Server
• MOLAP Server
• Front-end tool

Considering the above MOLAP Architecture:

1. The user requests reports through the interface.
2. The application logic layer of the MDDB retrieves the stored data from the database.
3. The application logic layer forwards the result to the client/user.

MOLAP architecture mainly reads the precompiled data. MOLAP architecture has limited capabilities to dynamically create aggregations or to calculate results that have not been pre-calculated and stored.

For example, an accounting head can run a report showing the corporate P/L account or the P/L account for a specific subsidiary. The MDDB would retrieve precompiled Profit & Loss figures and display that result to the user.

Cube Storage Layers

MOLAP servers typically implement two levels of storage representation, one for dense regions of the cube and another for sparse regions. This dual model keeps frequently queried slices in fast memory-resident structures while compressing rarely touched combinations on disk.

Key Points in MOLAP

• In MOLAP, operations are called processing.
• MOLAP tools process information with the same response time irrespective of the level of summarizing.
• MOLAP tools remove the complexities of designing a relational database to store data for analysis.
• MOLAP server implements two levels of storage representation to manage dense and sparse data sets.
• The storage utilization can be low if the data set is sparse.
• Facts are stored in a multi-dimensional array and dimensions are used to query them.

Implementation Considerations in MOLAP

• In MOLAP it is essential to consider both maintenance and storage implications when creating a strategy for building cubes.
• Proprietary languages are used to query MOLAP. However, they involve extensive click and drag support, for example MDX by Microsoft.
• It is difficult to scale because of the number and size of cubes required when dimensions increase.
• APIs should provide for probing the cubes.
• Data structures must support multiple subject areas of data analyses in which data can be navigated and analyzed. When the navigation changes, the data structure needs to be physically reorganized.
• A different skill set and tools are needed for the database administrator to build and maintain the database.

MOLAP Advantages

Below are the advantages of MOLAP:

• MOLAP can manage, analyze, and store considerable amounts of multidimensional data.
• Fast query performance due to optimized storage, indexing, and caching.
• Smaller sizes of data as compared to the relational database.
• Automated computation of higher level of aggregates data.
• Helps users to analyze larger, less-defined data.
• MOLAP is easier for the user, which is why it is a suitable model for inexperienced users.
• MOLAP cubes are built for fast data retrieval and are optimal for slicing and dicing operations.
• All calculations are pre-generated when the cube is created.

Disadvantages of MOLAP

Following are the disadvantages of MOLAP:

• One major weakness of MOLAP is that it is less scalable than ROLAP as it handles only a limited amount of data.
• MOLAP also introduces data redundancy as it is resource intensive.
• MOLAP solutions may be lengthy, particularly on large data volumes.
• MOLAP products may face issues while updating and querying models when dimensions are more than ten.
• MOLAP is not capable of containing detailed data.
• The storage utilization can be low if the data set is highly scattered.
• It can handle only a limited amount of data, therefore it is impossible to include a very large amount of data in the cube itself.

MOLAP vs ROLAP vs HOLAP

Choosing the right OLAP model depends on data volume, query patterns, and refresh cadence. The comparison below highlights how MOLAP differs from ROLAP and HOLAP.

• MOLAP: Stores precomputed cubes; fastest queries; limited scalability and dimension count.
• ROLAP: Operates directly on relational tables; scales to massive data; queries can be slower.
• HOLAP: Combines both; keeps summary data in cubes and detailed data in relational stores.

MOLAP Tools

Here are the popular MOLAP Tools:

• Essbase – Tools from Oracle that has a multidimensional database.

• Express Server – Web-based environment that runs on Oracle database.

• Yellowfin – Business analytics tools for creating reports and dashboards.

• Clear Analytics – Clear analytics is an Excel-based business solution.

• SAP Business Intelligence – Business analytics solutions from SAP.

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