MOLAP (Multidimensional OLAP)

MOLAP stands for Multidimensional Online Analytical Processing. It is an OLAP system where data is stored in multidimensional cubes, allowing for fast and efficient querying.

Key Characteristics of MOLAP:

• Data Storage: Data is pre-aggregated and stored in multidimensional cubes. Each cube stores data along multiple dimensions (e.g., time, location, product).
• Query Performance: Since data is pre-aggregated and stored in the cube, MOLAP systems offer very fast query responses.
• Usage: Ideal for performing complex calculations and analysis on large datasets quickly.
• Example: Microsoft Analysis Services, IBM Cognos, SAP BW.

Pros:

• Fast performance for queries and aggregations due to pre-calculation.
• Easy-to-use interface for users (with drag-and-drop capabilities in many tools).
• Efficient in handling complex multidimensional analysis.

Cons:

• Limited scalability; performance can degrade with extremely large datasets.
• Data might need to be periodically refreshed or reprocessed to reflect changes in source data.

ROLAP (Relational OLAP)

ROLAP stands for Relational Online Analytical Processing. It is an OLAP system that stores data in traditional relational databases and performs calculations dynamically when queries are made.

Key Characteristics of ROLAP:

• Data Storage: Data is stored in relational tables (e.g., SQL databases), not multidimensional cubes.
• Query Performance: Queries are calculated at runtime, based on the relational database schema, which can result in slower performance compared to MOLAP.