Updated March 21, 2023
Introduction to Operations in OLAP
OLAP helps in analyzing different extracts and in viewing business data from different points of view. It is often required to group aggregate and join data. The structure is basically called the OLAP cube. The OLAP cube is a data structure that is optimized for proper data analysis. It mainly consists of numeric facts which can be called dimensions, at the same time where the OLAP cubes are termed as ‘Hyper cubes’, which will allow the user to perform Multidimensional Analytical querying for the required data using the basic OLAP operations such as Drill-down, Roll-up, Slicing, Dicing and Pivot. A data warehouse should extract data from various data sources and formats. This data is then cleansed and transformed as per user needs. It is then loaded to the OLAP server where further analysis is done.
Types of Operations on OLAP
There are four types of OLAP operations that can be performed. These areas below:
- Roll up
- Drill down
- Slice and dice
- Pivot
Let us have a look at this one by one
1. Roll up
Roll up can be also considered as an aggregation of data. The data which is split up is consolidated and then used further. This can be done by following methods.
- Reducing dimensions.
- By using concept hierarchy where a system of grouping things is done based on a particular order or level.
Example
For a given set of data, the roll-up dimension technique can be used. By making use of the concept of hierarchy dimension reduction is done. This is done by combining the data across any axis. The above example has medals from four cities. Out of these 2 cities are from Asia while the other two are from Europe. If Roll-up operation is to be performed here, then it can be done by combining the Asian companies together and combining the European companies together.
Output:
This signifies more detailed data to less detailed data.
2. Drill down
Drilling down is nothing but breaking the data further into smaller parts. This dimension can also be applied to the data cube. Here the dimension is expanded. The expansion here is nothing but adding new dimensions to current data. If there is existing data, then it means that the current dimensions can be expanded. This expansion can take place along any axis of the data cube.
The process can be done by
- Going down the expected hierarchy while fragmenting.
- Increasing the dimension of current data sets.
Consider the following example where there are four countries C1, C2, C3, and C4. The population of these four countries per quarter is segregated by the area of that country. In order to drill down, we can see that there are two countries from Asia (C1 and C2) and the other two are from Europe (C3 and C4). If drill-down is to be performed then it can be done by expanding areas like countries, cities, districts, etc. Like this, it can be fragmented and reached to see the area of each country, city, district and even the smallest of the village.
3. Slice and Dice
In order to perform a slice operation, it is necessary to extract data from a single cube. This extract is used to form a new cube. If there is more data than one dimension it can also be extracted. This can be done from the same data cube which may result in different cubes from the one large cube. Continuing with the same example of four countries C1, C2, C3, and C4 where C1 and C2 are from Asia and C3 and C4 are from Europe. Continuing with this, the dice operation creates a subcube by selecting two or more dimensions from the present cube. Consider that there are of four companies C1, C2, C3 and C4 where C1 and C2 are from Asia and C3 and C4 are from Europe. By selecting aby two parameters from the different dimensions the dice operation can be performed easily. These can be either the population, area or geographical location. The only difference here is that you select two or more dimensions which will result in the creation of that subcube.
4. Pivot
Rotation of data cube’s orientation in order to check the other views that data can have is what is done when it is said that Pivot operation is being performed. To view data from a different perspective it rotates the data. It will provide the substitute presentation of the data. Once the subcube is obtained after the slice operation then the Pivot view gives it a new view. Consider there are four companies C1, C2, C3 and C4 where C1 and C2 are from Asia and C3 and C4 are from Europe. By rotating one dimension of the data cube we can easily perform the pivot operation. Say, you can change the area of the country from X-axis to Y-axis and Population per square kilometer from Y-axis to X-axis thus giving it a different view.
These four operations help in faster query performance.
Conclusion
The core of any OLAP system is numeric facts called measures. These measures can be further divided into dimensions. The measures are then placed at intersections that form the vector space. The OLAP cube is a matrix interface that will help in doing projection operations like aggregation. The cube metadata thus creates a star schema or snowflake schema which is then used in the relational database.
The measures are then divided into facts and dimensions on which new tables are created and different operations like cleansing and transforming of data take place. OLAP clients include programs like Excel, web applications, dashboards, etc. The optimized data thus is helping in determining different patterns and trends in any business. OLAP helps in achieving this target and makes analysis easier.
It will help in viewing business from different points of view. Different businesses make use of OLAP cube. The analytical operations thus making it easier to analyze data and predict patterns effortlessly and accurately.
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