OLAP stands for On-Line Analytical Processing. The key feature of OLAP is "Multidimensional." In other words, the ability to analyze metrics in different dimensions such as time, geography, gender, product, etc. For example, sales for the company are up. What region is most responsible for this increase? Which store in this region is most responsible for the increase? What particular product category or categories contributed the most to the increase? Answering these types of questions in order means that you are performing an OLAP analysis.
In this tutorial, you will learn:
An OLAP Cube is a data structure that allows fast analysis of data according to the multiple Dimensions that define a business problem. A multidimensional cube for reporting sales might be, for example, composed of 7 Dimensions: Salesperson, Sales Amount, Region, Product, Region, Month, Year.
There are four types of OLAP Operations:
Roll-up is also known as "consolidation" or "aggregation." The Roll-up operation can be performed in 2 ways
In this cube, the roll-up operation is performed by climbing up in the concept hierarchy of Location dimension (City -> Country)
In drill-down data is fragmented into smaller parts. It is the opposite of the rollup process. It can be done via
In this cube, the drill down operation is performed by moving down in the concept hierarchy of Time dimension (Quarter -> Month)
Slice selects a single dimension from the OLAP cube which results in a new sub-cube creation. In the cube given in the overview section, Slice is performed on the dimension Time = “Q1”
Dice selects a sub-cube from the OLAP cube by selecting two or more dimensions. In the cube given in the overview section, a sub-cube is selected by selecting following dimensions with criteria:
Pivot is also known as rotation operation as it rotates the current view to get a new view of the representation. In the sub-cube obtained after the slice operation, performing pivot operation gives a new view of it
This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. In essence, each action of slicing and dicing is equivalent to adding a "WHERE" clause in the SQL statement
MOLAP uses array-based multidimensional storage engines to display multidimensional views of data. Basically, they use an OLAP cube
HOLAP technologies attempt to combine the advantages of MOLAP and ROLAP. For summary-type information, HOLAP leverages cube technology for faster performance. When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational data