Introduction to Dimensions
The table of dimensions contains the company data. In fact tables of international key relations, the main keys of the dimensional tables are used. And the remaining columns in the dimension are natural information, which is the company’s object information. The size of an object is a topological measure of the size of its shell. In general, there are number of coordinates required to define the object’s point. A rectangle is, for example, two-dimensional and a cube three-dimensional. Often the dimensions of an object are referred to as “dimensionality.” In mathematics, this definition of dimension is important since the logical or visual complexity in a geometric object is precisely parametrized. The definition should potentially be generalized to abstract objects and can not be visualized explicitly. For eg, time can be viewed as one-dimensional, since it can be interpreted to be made up of only ‘now,’ ‘fore,’ and.’ Because time is as a line, an un-dimensional entity, no matter how far ahead and far into the future it is.
Top 9 Types of Dimension
Following are the Types of Dimension are explain below:
1. Conformed Dimensions
A dimension is considered a conformed dimension and is found in many places. A conformed dimension may be included in a single database or several data marts or data warehouses with several truth tables.
2. Role Playing Dimensions
A role-playing dimension is one in which more than one international key in the fact table may be added to the same dimension, along with its corresponding attributes. For starters, for the ship date and the shipping date, a fact table can contain international keys. But for and foreign key, the same date dimension features are valid, such that all foreign keys have the same dimension table added. The date component here plays several roles in the arrival and delivery date of the ship and hence its name.
3. Shrunken Dimensions
A shrunk dimension is another sub-set. For example, the fact table for orders which contain a foreign product main, but a foreign product segment, which is in the product table, but which is much less granular may be included in the target fact table. One way to deal with the heterogeneous grain condition is to create a smaller dimension table that has the crop group as its primary key. If the product dimension is black-snow, a different product type table is usually available, which will act as the shrunk dimension.
4. Static Dimensions
Static calculations are not taken from the original database but are generated within the data warehouse context. A statistic dimension, e.g. with status codes, may be loaded manually or produced by a process like a date or time dimension.
5. Degenerate Dimensions
When the dimension attribute is stored in the fact table and not in a separate dimension table, the degenerate dimension is given. In principle, these are dimensional keys for which no other attributes exist. In a data store, they are also used to evaluate the origins of an accumulated number in a report as a part of a question exercise. These values can be used to recover the OLTP device transactions.
6. Rapidly Changing Dimensions
An attribute of dimension that varies often is an attribute that changes rapidly. If you do not have to follow the changes, there would be no problem with the fast-changing attribute, but if you have to follow up the changes, a conventional methodology that changes slowly will create major inflation of the dimension. One solution consists of moving the attribute to its own dimension, with a different foreign key. This new dimension is referred to as quickly evolving.
7. Junk Dimensions
A single table with various characteristics and irrelevant functionality to preclude a significant amount of international keys from appearing on the fact table is a garbage factor. Junk measurements are also created to handle the international keys that shift easily.
8. Inferred Dimensions
A dimension record may not yet be ready when loading reality documents. One approach is to create a null substitution key for all other functions. Technically it should be considered a member inferred, but sometimes it is considered an assumed dimension.
9. Slowly Changing Dimensions
Attributes with a nature that would be prone to time shifts. The maintenance of a clear attribute history of improvements in the data warehouse relies on market necessity. This is regarded as a slowly changing characteristic and a slowly changing dimension is called the characteristic.
This is a guide to Types of Dimensions. Here we also discuss the introduction and top 9 types of dimensions along with a detailed explanation. You may also have a look at the following articles to learn more –