OLAP (Online Analytical Processing) is a methodology to provide end users with access to large amounts of data in an intuitive and rapid manner to assist with deductions based on investigative reasoning.

Online Analytical Processing (OLAP) Systems for Decision Support

IT organizations are faced with the challenge of delivering systems that allow knowledge workers to make strategic and tactical decisions based on corporate information. These decision support systems are referred to as Online Analytical Processing (OLAP) systems, and they allow knowledge workers to intuitively, quickly, and flexibly manipulate operational data using familiar business terms, in order to provide analytical insight.

OLAP systems need to:

  1. Support the complex analysis requirements of decision-makers,
  2. Analyze the data from a number of different perspectives (business dimensions), and
  3. Support complex analyses against large input (atomic-level) data sets.


There are two prominent architectures for OLAP systems: multidimensional OLAP (MOLAP) and relational OLAP (ROLAP). MOLAP architectures utilize a multidimensional database to provide analyses; their main premise is that OLAP is best implemented by storing data multi-dimensionally. In contrast, ROLAP architectures access data directly from data warehouses; ROLAP architects believe that OLAP capabilities are best provided directly against the relational database. When comparing these two architectures, the following observations can be made:

  • ROLAP leaves the design trade-off between query response time and batch processing requirements to the system designer, as the ROLAP architecture is neutral to the amount of aggregation in the database. MOLAP generally requires most of the database to be precompiled in order to provide acceptable query performance, thereby increasing batch processing requirements.
  • Systems with high data volatility, namely those with changing data aggregation rules and user-defined consolidations, require an architecture that can dynamically consolidate data for ad hoc and decision support analyses. ROLAP is very well suited for dynamic consolidations whereas MOLAP is biased towards batch consolidations.
  • ROLAP can scale to a large number of business analysis perspectives (dimensions), while MOLAP generally performs efficiently with ten or fewer dimensions.
  • ROLAP supports OLAP analyses against large volumes of input (atomic-level) data. In contrast, MOLAP provides adequate performance only when the input data set is small (fewer than five gigabytes).

5 Styles of BI White Paper Featuring Special Sections on OLAP Analysis » Download Now

The Case For Relational OLAP White Paper » Download Now

A Comparison of Standard OLAP and Relational OLAP Analysis

A. Single-click OLAP Manipulations allow people to slice-and-dice a subset of data to view it from many different perspectives

B. Relational OLAP Architecture allows people to "drill anywhere" in the entire relational database – across all dimensions and from summaries to transactional-level detail