Essbase Data and Metadata

Essbase on Autonomous AI Lakehouse organizes business data in a model that reflects how your organization analyzes it.

A data value tells you how much. Metadata tells you what the value means.

For example, a beverage company records unit sales of 1,000,000 for cola. The number becomes useful when you know its business context: the market, time period, product, and measure.

Data value             +  Business context                   =  Business meaning

1,000,000                 Market: United States                 Unit Sales of Cola
                          Year: FY26                            in the United States
                          Product: Cola                         during FY26
                          Measure: Unit Sales

In Essbase, an application contains a cube. A cube is an Essbase database that organizes data in three or more dimensions.

An outline defines the cube structure: its dimensions, members, and their relationships. A dimension is a category of business data. Its members are the values within that category.

In this example, Market, Year, Product, and Measure are dimensions. United States, FY26, Cola, and Unit Sales are members.

Cube outline
|
+-- Market
|   `-- United States
|
+-- Year
|   `-- FY26
|
+-- Product
|   `-- Cola
|
`-- Measure
    `-- Unit Sales

Essbase stores the value at the intersection of those members:

(United States, FY26, Cola, Unit Sales) = 1,000,000

When you use the cube, change the members to ask another business question. For example, select a different product, year, or market to compare unit sales.

You can slice and dice the data by selecting members in different dimensions, pivoting between business perspectives, and drilling from summary members to detail. This flexibility supports interactive, multidimensional (i.e. speed-of-thought) analysis: you can follow the next business question without changing the underlying business model or redefining the business logic.