Multidimensional database usage: In which environment is a multidimensional database model most commonly used for analytics and aggregated reporting?

Introduction / Context:
Multidimensional databases (cubes/tabular models) organize measures across dimensions (time, product, geography) to support fast slicing, dicing, and aggregation. Understanding where they fit clarifies modern analytics architectures.

Given Data / Assumptions:

• Goal: rapid summarized analysis over large historical data.
• Users: business analysts consuming KPIs and trends.
• Store: centralized analytical repository.

Concept / Approach:

Data warehouses integrate data from multiple sources, model them in star/snowflake schemas, and often expose OLAP cubes or columnar/tabular models for interactive analytics. While relational systems store detailed records, the multidimensional layer sits on top to accelerate aggregated queries and calculations.

Step-by-Step Solution:

Verification / Alternative check:

Examine BI stacks: ETL → warehouse (star schema) → OLAP cube → dashboards.

Why Other Options Are Wrong:

Relational/Hierarchical/Network: general data models; not specifically the multidimensional analytical layer.

Common Pitfalls:

Confusing the physical warehouse (relational) with the logical multidimensional model presented to users.

Final Answer:

Data warehouse