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Module 1: Data Warehousing and Business Analysis

20235m

In data warehouse technology explain ROLAP, MOLAP and HOLAP techniques of implementing a multidimensional view.

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ROLAP, MOLAP and HOLAP - Implementing the Multidimensional View

These are the three techniques used to physically implement the multidimensional (data-cube) view of a data warehouse.

1. ROLAP - Relational OLAP

The multidimensional view is implemented on top of a relational database: facts and dimensions are stored as relational tables in a star or snowflake schema, and every OLAP operation (roll-up, drill-down, slice) is translated into SQL queries with GROUP BY.

  • Handles very large and even detail-level data (scales with the RDBMS).
  • Uses aggregate tables, BITMAP indexes and join indexes to speed queries.
  • No fully pre-computed cube, so complex aggregations are slower; dependent on SQL tuning.

2. MOLAP - Multidimensional OLAP

Data (including all pre-aggregated summaries) are stored in a specialised multidimensional array / cube structure, not in rows of a table.

  • Queries are extremely fast because aggregates are pre-computed and stored.
  • Sparse cubes waste storage; cube-building (ETL) takes time and the data volume that fits in memory is limited - less scalable than ROLAP.

3. HOLAP - Hybrid OLAP

Combines both: detailed (base) data remain in relational tables while the aggregates are stored in a MOLAP cube.

  • Drill-through to detail uses SQL on the relational store; summary queries are answered from the fast cube.
  • Best balance of storage, performance and scalability - a compromise of ROLAP and MOLAP.

Comparison

Parameter ROLAP MOLAP HOLAP
Storage Relational tables Multidimensional arrays Detail in RDBMS, aggregates in cube
Data volume Very large, detail-level Moderate, summaries Large detail + summaries
Query speed Slower for complex aggregation Fastest (pre-computed) Fast for summaries, slower for detail
Pre-aggregation Limited / none Full Aggregates only
Scalability Best Limited by cube size Good
Best suited to Huge detail data, ad-hoc SQL Small-to-medium data needing speed Enterprises needing both

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