OLAP Concepts and Multidimensional Analytics Cheat Sheet

Back to Business IntelligenceUpdated 2026-05-15

OLAP (Online Analytical Processing) is a technology designed for fast, multi-dimensional data analysis on large datasets, enabling business intelligence through structured queries across dimensions like time, geography, and product hierarchies. Unlike OLTP systems that handle transactional workflows, OLAP specializes in complex aggregations, drill-downs, and cross-tabulations that power dashboards, trend analysis, and strategic reporting. The foundation rests on dimensional modeling — organizing data into fact tables (measurable events) and dimension tables (descriptive context) — allowing analysts to slice business metrics by any combination of attributes without rewriting queries. Understanding OLAP means mastering not just the cube operations and schema patterns, but also the performance trade-offs between storage architectures (MOLAP pre-aggregates for speed; ROLAP queries on-demand for flexibility) and the subtle art of declaring grain, designing slowly changing dimensions, and choosing the right modern columnar engine for your workload.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 87 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: OLAP Data Model Core ComponentsTable 2: OLAP Storage ArchitecturesTable 3: OLAP Cube OperationsTable 4: Dimensional Schema PatternsTable 5: Slowly Changing Dimensions (SCD)Table 6: Fact Table PatternsTable 7: Measure AdditivityTable 8: SQL Window Functions for OLAPTable 9: Window Function ClausesTable 10: SQL Advanced Aggregation FunctionsTable 11: MDX (Multidimensional Expressions) BasicsTable 12: Modern OLAP Database EnginesTable 13: OLAP Performance Optimization TechniquesTable 14: Data Warehouse Loading PatternsTable 15: Dimensional Modeling Best Practices

Table 1: OLAP Data Model Core Components

Component	Example	Description
Facts	`sales_amount`, `quantity_sold`, `profit`	Numeric measures stored in fact tables representing business events at a declared grain.
Measures	`SUM(revenue)`, `AVG(order_value)`, `COUNT(transactions)`	Aggregatable numeric values derived from facts; the quantitative data analyzed across dimensions.
Dimensions	`dim_date`, `dim_customer`, `dim_product`, `dim_geography`	Categorical attributes providing context for slicing and filtering facts; the "by" in analysis.
Hierarchies	`Year → Quarter → Month → Day` `Country → State → City`	Parent-child paths within dimensions enabling drill-down and roll-up navigation.
Grain	`One row = one order line item`	The most atomic level of detail captured in a fact table; defines what each row represents.
Star Schema	Fact table at center, denormalized dimensions radiate outward	Denormalized design with single-level dimension tables joined to central fact; optimized for query speed.

Table 1: OLAP Data Model Core Components

Component	Example	Description
Facts	`sales_amount`, `quantity_sold`, `profit`	Numeric measures stored in fact tables representing business events at a declared grain.
Measures	`SUM(revenue)`, `AVG(order_value)`, `COUNT(transactions)`	Aggregatable numeric values derived from facts; the quantitative data analyzed across dimensions.
Dimensions	`dim_date`, `dim_customer`, `dim_product`, `dim_geography`	Categorical attributes providing context for slicing and filtering facts; the "by" in analysis.
Hierarchies	`Year → Quarter → Month → Day` `Country → State → City`	Parent-child paths within dimensions enabling drill-down and roll-up navigation.
Grain	`One row = one order line item`	The most atomic level of detail captured in a fact table; defines what each row represents.
Star Schema	Fact table at center, denormalized dimensions radiate outward	Denormalized design with single-level dimension tables joined to central fact; optimized for query speed.