Unlike OLTP (online transaction processing), which is useful in transaction processing (involving less complex querying), OLAP deals with business intelligence and thus involves complex querying. OLAP allows for complex calculations and trend analysis, as well as intricate data modeling. OLAP lets the user perform ad hoc analysis of data in multiple dimensions, allowing for more informed business decisions based on multiple viewpoints.

For example, if a store manager wants to compare the profits of a specific product in Georgia with the profits of other products in the same store from March to September, then the store manager can analyze the data using OLAP. For this analysis, the store manager can use a multi-dimensional database, where each attribute is treated as a separate dimension. Examples of attributes include product, region, state, city, year, etc.

OLAP consists of measures and dimensions. From the above example, ‘profit’ is a numeric value which makes it a measure, while ‘year’ categorizes the measure and is therefore considered a dimension.

Types of OLAP

• MOLAP (Multi-dimensional OLAP)—Stores the data in multi-dimensional array.
• ROLAP (Relational OLAP)—Stores the data in relational database.
• HOLAP (Hybrid OLAP)—Uses a combination of MOLAP and ROLAP.

OLAP Cubes

OLAP Cubes are multidimensional databases where OLAP data is stored. These Cubes store data like traditional databases, but they are structured in a way that allows millions of multidimensional records to be analyzed at once.

Applications of OLAP

OLAP is typically utilized in tandem with business intelligence systems for the purpose of data mining, analysis and reporting.