A data warehouse OLAP (Online Analytical Processing) cube is a multidimensional representation of data, designed to support business intelligence and data analysis. It's a powerful tool for analyzing data from multiple angles, providing insights and answers to complex questions.
Basic Analytical Operations of OLAP
Roll-up: Roll-up is also known as "consolidation" or "aggregation." It can be performed by reducing dimensions or climbing up concept hierarchies. For example, you can roll up sales data from cities to countries.
Drill-down: Drill-down is the opposite of roll-up. You can move down the concept hierarchy to analyze specific details. For instance, you can drill down from quarters to months or years.
Slice and dice: Slice selects a single dimension from the OLAP Cube, creating a new sub-cube. Dice selects multiple dimensions with specific criteria, allowing you to analyze data from different perspectives.
Pivot: Pivot is also known as rotation. It rotates the current view to provide a new perspective on the data. For example, if you've sliced your sales data by region and product, you can pivot to see the data in a different way, such as by month or year.
Types of OLAP Cube
Multidimensional Online Analytical Processing (MOLAP): MOLAP cubes are based on a hierarchical data model, where dimensions and facts are organized into a tree structure. The data is pre-aggregated at the lowest level of detail.
Relational Online Analytical Processing (ROLAP): ROLAP cubes are built on top of relational databases. Data is aggregated and stored in a star schema, with fact tables and dimension tables linked through foreign keys.
Hybrid Online Analytical Processing (HOLAP): HOLAP combines MOLAP and ROLAP to leverage the strengths of both approaches. It allows for complex calculations on large data sets, while still maintaining the flexibility to analyze data from multiple perspectives.
Example OLAP Cube Scenario
Suppose a retail company wants to analyze sales data by product, time period, and region. They can create an OLAP cube with dimensions such as:
Product
Date
Region (e.g., North America, Europe)
And facts like:
Sales Amount
Sales Count
This diagram illustrates the structure of an OLAP cube, with dimensions and facts represented as a hierarchical tree.