What is a Data Cube in a Data warehouse?
In a data warehouse, the data is stored in a multidimensional structure called a data cube. This data cube was initially intended for use by OLAP tools, which would benefit significantly from having quick and easy access to the data's many dimensions. In contrast, data mining is another viable application for the data cube.
The data cube is a dimensional and factual representation of the information. The compiled information is shown in the form of a data cube. The two most common types of data cube are multidimensional and relational.
Data Cube: What Is It?
Data cubes are multidimensional data models that hold summarised or aggregated information in a way that makes it easier for online analytical processing (OLAP) tools to perform analyses quickly and efficiently. Data cubes save time in online research by storing computed data in a standardized format.
Most of us picture a three-dimensional cube when we hear the word, but in data warehousing, cubes can have any number of dimensions.
A data cube uses dimensions and facts to represent the information it stores. The entities or perspectives from which an organization would like to keep its data serve as the cube's "dimensions."
There is a dimension table for each dimension, and in it, you may find more information about that dimension. Some possible attributes of a branch dimension include branch name, branch code, branch address, and so on.
Facts provide the basis of every multidimensional data model, such as a data cube. Information about an attribute can be found in a fact table.
The data in the fact table are presented in numerical formats, such as the total units sold or revenue generated by a specific location or period. Once we've mastered the data cube, we can move on to determining how they should be categorized.
Classification of Data Cubes:
Below, we will go over the two primary types of data cubes:
1)Multidimensional Data Cube (MOLAP):
To guarantee a comprehensive understanding of the data, it is typically stored as a multidimensional array. When it comes to storing large amounts of information, a multidimensional data cube is a helpful tool.
To facilitate data access, retrieval, and storage, multidimensional data cubes use indexing to represent each dimension of a data cube.
2)Relational Data Cube (ROLAP):
As an "expanded version of relational DBMS," the relational data cube can be viewed as a powerful tool. Each dimension of a data cube corresponds to a set of relational tables used to hold the requisite information.
领英推荐
While both relational and multidimensional data cubes use SQL to execute aggregate calculations, the latter is significantly faster than the former. However, the relational data cube can accommodate ever-increasing amounts of data.
A hybrid data cube combines features from both the relational data cube and the multidimensional data cube. Features such as scalability from the relational data cube and quicker computation speeds from the multidimensional data cube are both retrieved by the hybrid data cube (HOLAP).
Operations on Data Cube :
The following sections cover four of the most fundamental methods for manipulating data cubes:
1)Roll up: The roll-up process can either execute dimension reduction or concept hierarchy to summarise or aggregate the dimensions.
2)Pivot: The pivot is not a mathematical process but rather a rotation of the data cube that allows for a new perspective on the data in all of its dimensions.
3)Drill Down: Information along any dimension is broken down into smaller pieces whenever the drill-down procedure is carried out.
4)Slice and Dice: The data cube is "sliced" by picking out a single dimension and "dicing" it into a new cube. Subcubes can be formed by selecting more than one dimension using the dice operation.
Merits of Data Cube:
1)The data cube makes it simple to compile and summarise information.
2)Data cubes are a more effective means of displaying information.
3)Large amounts of information can be stored in a data cube, but the structure is so straightforward that even a kid could use it.
4)The data cube improves the data warehouse's overall performance.
5)The data cube's aggregated information allowed for quick analysis and decreased response times.
Conclusion
In the world of budgeting and planning Hyperion essbase was one of the first to do it! Since then many have followed. Anush K. How difficult is it to build and integrate into a newly created Postgres db?