There are three reasons to use an OLAP cube in addition to your relational database – performance, drill-down functionality and availability of software tools.
Online analytical processing (OLAP) is a technique for quickly analyzing a measure, e.g. profit margin, by multiple categories or dimensions, e.g. customer, region, fiscal period and product line. Typically the end user software has capabilities to drag categories to rows and columns and aggregate the measure at each intersection of a row and column (often called a cross tab report). This is similar to the familiar spreadsheet format. This numeric format can usually also be represented in the form of a chart or graph. The real power of OLAP is the ability to drill down on a category to see more details. For example, you might drill down on a state to see details by city.
An OLAP cube is a technology that stores data in an optimized way to provide quick response to queries by dimension and measure. Most cubes pre-aggregate the measures by the different levels of categories in the dimensions to enable the quick response time. End user software will make querying a cube very easy, but developers, who may be accustomed to using SQL language, will need to learn a new language – MDX (Multi-Dimensional eXpressions).
The standard design for a relational database source for this analysis is called a star schema. A fact table is related to multiple dimensions and this can be represented graphically in a form of a star. A star schema design will support reporting and analysis by dimensions for measures in the cross tab and graphical formats without using an OLAP cube. Why would you go to the time, expense, disk space, skill development and increased maintenance to also build a cube when a relational database will support this analysis?
There are three reasons for adding a cube to your solution:
- Performance. A cube’s structure and pre-aggregation allows it to provide very fast responses to queries that would have required reading, grouping and summarizing millions of rows of relational star-schema data. The drilling and slicing and dicing that an analyst would want to perform to explore the data would be immediate using a cube but could have lengthy pauses when using a relational data source.
- Drill down functionality. Many reporting software tools will automatically allow drilling up and down on dimensions with the data source is an OLAP cube. Some tools, like IBM Cognos’ Dimensionally Modeled Relational model will allow you to use their product on a relational source and drill down as if it were OLAP but you would not have the performance gains you would enjoy from a cube.
- Availability of software tools. Some client software reporting tools will only use an OLAP data source for reporting. These tools are designed for multi-dimensional analysis and use MDX behind the scenes to query the data.
OLAP cube technology will cost more in terms of development, learning and project time but will return benefits in fast response time to analyze large amounts of data. This capability can result in insights that drive actions and decisions that enable very large organizational productivity, cost saving or revenue increasing gains.