From Relational Data to a Business Model

If you work with relational data, you may think in terms of fact tables, dimension tables, joins, and SQL. Essbase on Autonomous AI Lakehouse adds a multidimensional business model for analyzing that data.

In this model, dimensions represent the business perspectives by which users analyze data, such as Product, Market, and Time. Hierarchies organize members from summary to detail, and measures or accounts represent the values being analyzed. Calculations express reusable business rules.

Instead of repeatedly rebuilding joins, filters, and aggregation logic for each analysis, you can define the business model once and let users explore it through the Essbase web interface or Smart View.

Relational or Lakehouse concept	Essbase business model	Analysis benefit
Fact table	Cube data	A consistent analytical view
Product, market, or date columns	Dimensions	Pivot and filter by business perspective
Dimension-table relationships	Hierarchies and members	Drill from summary to detail
Revenue, cost, or units columns	Measures or accounts	Analyze business values
Repeated SQL formulas	Calculations	Apply governed business logic
SQL queries and joins	Pivot, slice, drill, and retrieve	Explore related business questions