What is dimensional modeling

In our example, windbreaker sale is the fact that we want to measure. Date, store location California and Pennsylvania , and product type Nylon windbreakers and Polyester windbreakers are the dimensions that give us further insights into the sales process. Each record in the dimension table should have a unique key.

This key will be used to uniquely identify the records in the dimension table and will be used as the foreign key in the fact table to reference the particular dimension and join it with the fact table. Tables show the dimension table for each of the dimensions in our apparel line example.

Granularity refers to the level of information that is stored in any table. For instance, in our example, the sales amount is recorded on a daily basis, therefore the granularity in this case is daily. The fact tables in a dimensional model should be consistent with the pre-defined granularity.

An important feature of dimensional models is that the dimensional attributes can be easily modified without changing the complete transaction information. For example, the apparel line decides to continue the Nylon windbreaker from Fall Collection into the Spring Collection and updates the name in the Collection attribute. Making the update is an easy process in the dimensional table, but with the update we will lose our previous data.

If the goal of your data modeling and data warehouse is maintaining and storing history, this could be a problem. Dimensions that change slowly over time are called Slowly Changing Dimensions. You can maintain and store historical data by tracking slowly changing dimensions. Read more about different use cases of slowly changing dimensions. Design, test, launch, and implement data warehouse from scratch, and automate processes to deliver insights quickly without writing a single line of code.

Designing dimensional models is an essential step in building the framework of an enterprise data warehouse. The process can be streamlined with the help of a robust data warehouse automation tool such as Astera Data Warehouse Builder. With Astera DW Builder , you can quickly build dimensional models in a visual code-free integrated development environment.

Entities can be denormalized with simple drag-and-drop and merges. Entity roles facts and dimensions can be assigned in bulk, which can save you valuable time when working with hundreds of entities. They are given below:. A Conformed Dimension is a type of Dimension that has the same meaning to all the Facts it relates to.

An Outrigger Dimension is a type of Dimension that represents a connection between different Dimension Tables. A Shrunken Dimension is a perfect subset of a more general data entity. In this Dimension, the attributes that are common to both the subset and the general set are represented in the same manner.

A Role-Playing Dimension is a type of table that has multiple valid relationships between itself and various other tables. Common examples of Role-Playing Dimensions are time and customers.

They can be utilised in areas where certain Facts do not share the same concepts. This type of table is a table in the Star Schema of a Data Warehouse. Each Dimension corresponds to a single Dimension Table. A Junk Dimension is a type of Dimension that is used to combine 2 or more related low cardinality Facts into one Dimension. A Degenerate Dimension is also known as a Fact Dimension. They are standard Dimensions that are built from the attribute columns of Fact Tables. Sometimes data are stored in Fact Tables to avoid duplication.

A Swappable Dimension is a type of Dimension that has multiple similar versions of itself which can get swapped at query time. The structure of this Dimension is also different and it has fewer data when compared to the original Dimension.

The input and output are also different for this Dimension. This is a type of Dimension that explains where a particular step fits into the process. Each step is assigned a step number and how many steps are required by that step to complete the process.

To explore about the types of Dimensions in detail, click this link. Dimensional Data Modelling requires certain analysis on the data to understand data behaviour and domain. The typical architecture of a DDM is shown below:. The business process helps to identify what sort of Dimension and Facts are needed and maintain the quality of data.

Identification of Grain is the process of identifying how much normalisation lowest level of information can be achieved within the data. It is the stage to decide the incoming frequency of data i.

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Data Mining. Most of the fact table rows are numerical values like price or cost per unit, etc. In this step, you implement the Dimension Model. A schema is nothing but the database structure arrangement of tables.

There are two popular schemas. The star schema architecture is easy to design. It is called a star schema because diagram resembles a star, with points radiating from a center. The center of the star consists of the fact table, and the points of the star is dimension tables.

The fact tables in a star schema which is third normal form whereas dimensional tables are de-normalized. The snowflake schema is an extension of the star schema. In a snowflake schema, each dimension are normalized and connected to more dimension tables. Multidimensional data model in data warehouse is a model which represents data in the form of data cubes. It allows to model and view the data in multiple dimensions and it is defined by dimensions and facts.

Multidimensional data model is generally categorized around a central theme and represented by a fact table.