How to Optimize OLAP Cubes with Rubik: Dimensional Reduction Strategies Explained

This article walks through Rubik's OLAP cube reduction techniques—including aggregation groups, required, combined, derived, hierarchical, and partial cubes—by designing and implementing buyers and suppliers cubes with six tables, demonstrating performance gains through pre‑computed queries and SQL examples.

CubeDimensional ReductionOLAP

0 likes · 10 min read

How to Optimize OLAP Cubes with Rubik: Dimensional Reduction Strategies Explained

StarRing Big Data Open Lab

Aug 18, 2017 · Big Data

Cut OLAP Cube Storage Explosions: Proven Dimensionality Reduction Tricks with Rubik

This article explains why raw OLAP Cubes consume exponential storage, then details six practical dimensionality‑reduction methods—Aggregation Group, Mandatory Dimension, Joint Dimension, Derived Dimension, Hierarchy Dimension, and Partial Cube—showing how each can dramatically shrink materialized tables while preserving query performance.

Cube OptimizationData WarehousingOLAP

0 likes · 10 min read

Cut OLAP Cube Storage Explosions: Proven Dimensionality Reduction Tricks with Rubik

StarRing Big Data Open Lab

Jul 21, 2017 · Databases

How Materialized View Based Optimizer (MBO) Supercharges Inceptor Queries

The article explains the Materialized View Based Optimizer (MBO) in Inceptor, detailing its purpose, configuration parameters, supported aggregation, filter, and join scenarios, and shows how combining MBO with Transwarp Rubik cubes can dramatically accelerate OLAP query performance.

InceptorOLAPQuery Optimizer

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How Materialized View Based Optimizer (MBO) Supercharges Inceptor Queries