OLAP vs MOLAP vs HOLAP

These are three server architectures for implementing OLAP, differing in where and how the multidimensional data is physically stored.

ROLAP (Relational OLAP)

• Stores data in standard relational tables (star/snowflake schema).
• Multidimensional operations are translated into SQL at query time.
• Pros: scales to very large data volumes, reuses existing RDBMS technology.
• Cons: slower query performance (SQL translation overhead), limited by SQL's multidimensional expressiveness.

MOLAP (Multidimensional OLAP)

• Stores data in a proprietary multidimensional array/cube structure (pre-computed).
• Pros: very fast query response (data pre-aggregated), rich analytical functions.
• Cons: cube-build time can be long, storage can explode for high-cardinality/sparse dimensions ("data explosion"), limited scalability for very large datasets.

HOLAP (Hybrid OLAP)

• Combines both: detailed data stays in relational tables (ROLAP), while summary/aggregated data is stored in MOLAP cubes.
• Pros: balances MOLAP's speed for aggregates with ROLAP's scalability for detailed data.
• Cons: more complex architecture to build and maintain.

Comparison Table

Aspect	ROLAP	MOLAP	HOLAP
Storage	Relational tables	Multidimensional array/cube	Mixed (relational + cube)
Query speed	Slower (SQL translation)	Fastest (pre-computed)	Balanced
Scalability	Very high (large data)	Limited (cube size)	High
Storage overhead	Low	High (sparse data explosion)	Moderate
Best for	Very large, detailed datasets	Fast, smaller/aggregated cubes	Mix of both needs

ROLAP:  [Relational DB] ──SQL──▶ query engine ──▶ result
MOLAP:  [Precomputed Cube] ──direct lookup──▶ result   (fast!)
HOLAP:  [Detail: Relational] + [Summary: Cube] ──▶ result (best of both)

In short: ROLAP trades speed for scalability, MOLAP trades scalability for speed, and HOLAP tries to get both by splitting detail vs summary data across the two storage models.