In this article, we will understand a little more about data modeling with respect to learning Power BI.
Data modeling is the act of using words and symbols to represent data and how it flows in a simplified representation of a software system and the data bits it includes. Data models serve as a roadmap for creating a new database or altering an existing one. It is a process of defining how your tables are related to each other. It is also where you create your calculated columns, calculated tables, custom measures, etc. Data modeling enables a firm to make the most use of its data in order to satisfy business information demands. It enables business and technical workers to collaborate on how one can store, access, share, update and use data inside of an organization.
Why is data modeling done in the first place? Few of its objectives are the following:
Data modeling contributes to the development of internal data standards and uniform data definitions. This is common in the area of data governance projects. It is also important in the data architecture process, which records data assets and maps how data moves across IT systems in order to develop a conceptual data management framework. Data modeling has become an important tool for both data analysts and data scientists involved in the development of BI tools as well as involved in more complicated data science along with advanced analytics.
Data models can divided into various types.
The following are the different kinds of data models:
A conceptual data model is a pictorial representation that outlines the business ideas that are entities, as well as the interactions between them, in order to learn, showcase, and record a data-driven insight of the organization’s operation. These models are a high-level graphical representation of data of a business. They are not, however, bound to any particular database or application technology. Domain models are another term for this since they provide a big-picture perspective of what the system will comprise. These models are often created as part of the original project specifications’ collection process. One often expresses such models using an ERD (Entity Relationship Diagram) or an ORM (Object Role Model). They either have just important attributes or none at all.
It’s time to get more precise using a logical data model after your original concepts have become evident through conceptual data modelling. Logical data models depict the relationships between data items and represent the data from a technical standpoint. These models also clarify the data properties that define such entities, as well as the relationships that are between them.
Basically, a logical data model provides as much information about the data as necessary. This model specifies the primary key for each entity. All foreign keys, or keys that identify the connection between different units, are specified. Normalization also occurs or takes place in a logical data model.
To create such a model, only five steps are necessary.
The steps are as follows:
DBAs (Database Administrators) and developers are often the ones that build a physical data model. A physical data model explains how the system would be executed using a certain database management system. This model is database-specific that depicts relational data items like tables, columns, and relationships between these items. Primary and foreign keys, indexes, authorizations, and so on are all specified in this model. Here, columns should have specific lengths, datatypes, and default values. Database designers take the help of physical data models to build database designs.
The following are some examples of how to make such a model:
There are over four types of physical data models and they are flat-file, hierarchical, relational and object-oriented types.
Let us understand these types a little more in detail.
The following are few advantages of data modeling: