By combining dimensions and information, Ralph Kimball came up with Dimensional Modelling. SELECT OPERATION is best suited to this approach because its primary objective is to enhance data retrieval. Using this model, we can store data in a way that makes it easier to save and retrieve data once it has been stored in a data warehouse. Many OLAP systems employ the dimensional model as their primary data structure.
Storage in a data warehouse can be made more efficient by using a technique called Dimensional Modelling (DM). Dimensional modelling is used to speed up data retrieval by making the database more efficient. Ralph Kimball came up with the idea of “fact” and “dimension” tables when he established Dimensional Modelling.
Dimensional models in a data warehouse are designed to read, summarise and evaluate numeric information such as values, balances and counts as well as counts and weights, among other things. When used in a real-time OTS, relational models are better suited for data updates and deletions.
Different data structures, such as the dimensional and relational models, have different advantages for storing data.
Normalization and ER models, for example, decrease data redundancy in a relational manner. The dimensional model in the data warehouse, on the other hand, arranges data in such a way that information can be more easily retrieved, and reports may be generated.
As a result, dimensional models are more commonly found in data warehouses than in relational databases.
It is our goal in this post to introduce you to the fundamentals of dimensional modelling and the concepts associated with it. Different tools and methods of implementation will also be discussed in order to properly design dimensional data models.
Disciplinary guidelines for dimensioning
Dimensional modelling is governed by the laws and principles listed below:
● A single fact table should have the same amount of detail for all facts.
● A surrogate key must be used for dimension tables
● Incorporate dimensional models into your business processes.
● Every fact table must have a date dimension table connected with it.
● Load atomic data into dimensional structures, then export.
● Deliver a business solution that balances the needs of the customer with the reality of the market.
● Dimension tables are required for storing report labels and filtering domain values.
Dimensional modelling has many advantages
Because of the advantages it provides, dimensional modelling is still the most popular method for developing enterprise data warehouses. Among them are:
● Because of the standardisation of dimensions, data can be reported consistently across departments.
● The history of dimensional data is stored in dimension tables.
● A completely new dimension can be introduced without causing severe disruptions to the facts.
● Moreover, dimensional data storage allows for easier retrieval of information from the database once the data has been entered.
● Dimensional tables are easier to comprehend than the normalised model.
● Clear and basic business categories are used to categorise and organise data.
● The dimensional model is easily understood by the company. Each fact, dimension or characteristic in this model is defined in terms of the business.
● Denormalization and optimization of dimensional models for quick data querying are commonplace. There are many relational database solutions that take this model into account and improve query execution plans in order to help with performance.
● An efficient data warehouse schema can be achieved through the use of dimensional modelling. There will be fewer joins and less data redundancy as a result.
● The dimensional model also aids in query performance, which is another benefit. Because it is more denormalized, it is better suited for querying than a more conventional database.
● Change can be accommodated in dimensional models without a problem. Existing business intelligence applications using dimension
tables do not need to be impacted by the addition of new columns to those tables.
5 Steps to Design A Dimensional Data Warehouse
Creating Dimensional Data Modelling Using these steps
1. Determine the Business Process: Identifying the company’s goals is the first step in the process. Depending on the needs of the company, examples include sales, human resources, and marketing. When it comes to Data Modelling, the quality of the data available for that process plays a vital role in determining the business objective.
2. Granularity – Identifying: The table’s lowest granularity level is called the granule. Grain provides a detailed description of the business problem and its solution.
3. Identify Dimensions and Their Qualities: Objects or things are what dimensions are. Data warehouse facts and measurements are classified and described by dimensions in a way that helps businesses get relevant answers to their questions. Dimension tables in a data warehouse contain columns for
each descriptive attribute. For example, a year, month, and weekday could be included in the data dimension.
4. Identify the Fact: The fact table contains the quantifiable information. Prices, cost per unit, and so on make up most entries in a fact table.
5. Build Schema: The Dimension Model is put into place in this phase of the process. The structure of a database can be referred to as a schema (arrangement of tables). There are two common models.
● Star Schema:
The star schema architecture is simple to implement. Because the diagram looks like a star with points radiating from a central point, it is known as a “star schema.” The star’s core is the fact table, and its points are dimension tables.
Star schemas have fact tables in the third normal form, whereas the dimensional tables are de-normalized in the second normal form.
● Snowflake Schema:
Extending the star design, we have a snowflake model as well. Each dimension in a snowflake schema is normalised and linked to other dimension tables.
A dimensional model is a data structure technique that has been optimised for use with data warehousing software. The measurements/metrics or facts from your business process are known as facts. A business process event’s context is provided by dimension. The many aspects of dimension modelling are referred to as attributes. The first of the five steps in dimensional modelling is to identify the business process. 2. Recognize the grain (level of detail) 3. Determine the dimensions 4. Recognize Facts 5. Create a Star