Data Modeling Tutorial for Beginners

Data Modeling is the process of organizing the data in a structured manner in a particular database. In business domains, data models play a vital role in understanding any data in-depth. If the data is retained in an unstructured and unorganized manner, it will be of no use. Every person has his or her perspective of viewing data. It is necessary to transform any raw data into an understandable form. Hence, if the data is transformed into a particular model, all people can view them similarly. 

What is the Data Model?

A data model implies transforming the raw data or information into an abstract model, which is understandable by the ordinary people. Any data is composed of several elements, like its structure, properties, attributes, etc. A data model is responsible for organizing all the elements of data and relating them with each other. To make a clear understanding of the data model concept, we shall see an example.

For example, consider a data model car. A car is composed of several components and has different properties. The data elements of the data model car can be its color, size, structure, etc. Hence, the data model car organizes all its data elements and represents relationships between them. 

The data model represents how the data is related to each other and how it should be organized. It does not focus on what operations to be carried out on the particular data. 

What is Data Modeling?

Data Modeling is the process of introducing the data model for the particular data saved in the database. In other words, data modeling implies representing data objects or elements conceptually and a relationship between several data objects. Data modeling is essential in business processes, as it assists business people to analyze the data quickly with its theoretical representation. 

In simple words, data modeling refers to representing the intricate software design in the form of understandable diagrams and displaying the relationship between every part of the software design using symbols and text. 

We know that architects make out a plan of the building on the paper before constructing any building. They have a precise idea and a clear picture of constructing the building. Similarly, the data model is also a visual representation of data in an organized way that relates its objects. However, professional data modelers are required to design a data model. They interact with the clients or users having the raw data and transform it into a structured and understandable format.

Why do we do Data Modeling?

What is the goal of data modeling? Why do we do data modeling? Let us see some critical reasons for using data modeling.

  • The data model’s primary objective is to represent any necessary information in an organized and structured manner and understandable by ordinary people. 
  • Another reason for using the data model is that it represents every data object required to be present in the database. If any part of the data is left to add in the database, it may create a false report and generate an errored outcome. 
  • The data model creates the database schema and defines primary keys, foreign keys, authorizations, access rights, and stored procedures
  • Another need for the data model is it designs a database at three different levels, the conceptual phase, the logical phase, and the physical phase. 
  • If your data has duplicate and missing information, the data model eliminates the redundant data and finds out the missing data. 
  • Developers get a clear idea about the particular data and can develop the database quickly and effectively. 

Phases of Data Model

Any data model is designed using three phases: the conceptual data model, the logical data model, and the physical data model. Let us see each of these data model phases in detail. 

Conceptual Data Model:

What is the database conceptual model? The conceptual database model defines what data does the database contains. In the conceptual data model phase, only clients and data modelers are involved. The client has the information system, and data modelers organize the data from the information system into a specific model. Data modelers represent the client’s requirements and business concepts in the data model. 

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Generally, the conceptual phase includes entities, attributes, and relationships between the database’s data entities. In the conceptual data model, the database structure is not involved. Instead, it only focuses on the rules and concepts of the business data entities, their relationships, and their attributes. 

Entity: An entity in the database is an object or thing in the real world. For example, any place, any single person, or an item is an entity. 

Attributes: Attributes are properties of the entity. They store all information about a particular entity. 

For example, consider an entity student. Attributes of an entity students can be a name, identity number, age, class, and address. All these attributes provide information about a specific student, 

Relationship: A relationship implies the connection between any two entities. The below example will make you clear about the relation between two entities. 

Consider the two entities, Customer and Product. The Customer entity has attributes, like customer name and customer ID. Next, the Product entity has attributes, like product name and product price. Customers buy a particular product after seeing its price, which is referred to as the deal. So, the deal is the relationship between Customer and Product entities.  

Features of the Conceptual Data Model

The conceptual data model represents what data the database consists of. The following are some significant characteristic of the conceptual data model:

  • The conceptual data model covers a wide range of business rules and concepts. 
  • Data modelers design this stage of data model only for the business stakeholders or audiences, to make them aware of what data the database has. 
  • In the conceptual data model, only the data is represented in the form of a real-world entity. It is not concerned with data location, data storage capacity, or any other software specifications. 
data modeling

Logical Data Model:

In the above section of the article, we have seen what the conceptual database model is. Now, we shall know what the logical database model is. In the conceptual data model, we define entities and their attributes. No other database information is released. In the logical database model, we describe the structure of the data attributes or data elements. In simple words, we describe the type of attributes, like integer, string, character, etc. This data model phase represents how the database should be implemented. 

We have discussed one example in the conceptual data model. It has two entities, Customer and Product. We define customer name and customer ID as the Customer’s attributes and the product name and product price as the Product’s attributes. 

In the logical database model, we need to represent the type of each attribute of entities. Hence, the customer name attribute will have the String type, and the customer ID attribute will have an Integer type from the Customer entity. Similarly, the product name will have the String type, and the product price will have the Float type from the Product entity. 

Characteristics of the Logical Data Model

The logical database model defines the data type of each attribute. Here are some primary characteristics of the logical data model:

  • All attributes of every single entity will have data types.
  • The logical data model is implemented independently from the entire database. 
  • It does not specify any primary or secondary key of entities. 
  • The logical data model follows the database normalization up to 3rd Normal Form (3NF)
  • Business Analysts and data architects handle the implementation of the logical database model.
  • A single project’s data requirements can be used for many other projects depending upon the project scope.

Physical Data Model:

The physical data model is the last phase of any data model. The conceptual and logical data model phases act as the foundation for the physical stage. In the physical data model, the database-specific implementation of the data model takes place. It generates the database schema for the data model due to the physical data model’s meta-data. The physical data model depicts how the data model is implemented using the database management system. 

This data model defines the database schema; it specifies the constraints, primary keys of attributes, indexes, and other database management system features. It deals with database storage capacity, location, and software specifications. 

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Features of the Physical Data Model

Below are some primary characteristics of the physical data model:

  • The physical data model represents the implementation of the data model using the database management system. 
  • It specifies the location of the data, data storage capacity, and the methodology used. 
  • The database schema is developed in the physical data model. It is necessary to define foreign keys, primary keys, access profiles, authorizations, etc. 
  • Every attribute or column should be defined with the data types, length or storage capacity, and values. 

Types of Data Model

Typically, there are four commonly used different kinds of data models. They are as follows:

Hierarchical Model:

One of the data model kinds is the hierarchical model. In this data model, the data is represented in a tree-like structure. It has one root node called the parent node. The root node or parent node has child nodes. Further, these child nodes act as parent nodes of their child nodes, and so on. Every child node in the tree-like structure has only one parent node, but one parent can have multiple child nodes. Hence, the relationship between parent and child nodes is one-to-many. 

One real-life example of the hierarchical model is students and courses. One single student can select multiple courses. Another example is shopping sites. If you click on the shoe category, you need to choose men’s or women’s shoes. After that, they provide various sub-categories, like sneakers, sports shoes, heels, etc. 

Advantages:

  • The hierarchical data model is straightforward to use, as it has a tree-like structure. 
  • Another significant advantage of the hierarchical model is it maintains data integrity. If there is any modification in the parent node, its child node gets modified automatically. 

Disadvantages:

  • As this data model is very straightforward, it does not support complicated relationships. 
  • Any change in the parent node is seen in its child node. Hence, if we delete the parent node, the child node also gets deleted. 
  • No child node can have two different parent nodes. 

Entity-Relationship Model:

The entity-relationship model is one of the commonly used database models. It involves entities, attributes, and relationships. This data model type is specially meant for describing real-world examples. The entity-relationship data model is the most straightforward type that is understandable by clients and ordinary people. It represents information in the form of diagrams. 

Any new developers can get a precise idea about any project by looking at the relational model. The entity-relationship model is also referred to as the ER model. Every ER model consists of entities, attributes, and relationships. An entity is any real-world object. The relationship implies a connection between any two entities. Lastly, attributes mean the properties of the entity. 

Advantages:

  • The entity-relationship model generation is very straightforward and effortless. But, you must exactly know which attributes are related to which entities. 
  • This database model is widely used by software developers to quickly their ideas through diagrams. 
  • The entity-relationship model is the flexible model as it gets transformed into any other data model. 

Disadvantages:

  • There are no specific notations used in the entity-relationship model. So, every developer can use the desired notations, which cannot be understandable by others. 
  • The entity-relationship model represents a high-level view of information. Hence, some information may remain hidden. 

Network Database Model:

The network database model uses the same concept as that of the hierarchical data model. It also represents data in the tree-like structure. Unlike the hierarchical model, a single child node can have two parents in the network database model. Graphs replace the tree-like system in the network database model. A single child node can relate two different parent nodes. 

For example, a single student at a particular college can be a part of the computer science department and the library. But, in hierarchical mode, students could relate only to one node. Using the network database model, you can access a particular parent node through two different paths. It supports one-to-one and many-to-many relationships. 

Advantages:

  • The network database model has a relatively faster speed than the hierarchical database model. It can access any record quickly as each record can have several different paths. 
  • Data integrity is also supported in the network database model. As it is a tree-like structure, any change in the parent node reflects in its child node. 

Disadvantages:

  • In the network database model, database operations, like inserting, deleting, and updating data, are very intricate. 
  • This model has multiple relationships. Hence, having multiple relationships can make your system or database more complicated. 

Relational Model:

Another commonly used database model is the relational database model. This type of data model is also understandable by ordinary people. All information or data is represented in the form of two-dimensional tables, i.e., rows and columns. These tables are referred to as relations. 

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Each relation has a specific number of rows and columns. A row is called a tuple, and the column is called an attribute or a filed. Consider a relation, students, having a name, age, ID, and department as attributes. Tuples of the student’s relation contain information about the instance of the object. 

NameAgeIDDepartment
John183031CSE
Samuel193032CSE
Watson183033CSE
Students

Advantages:

  • The relational data model is another most straightforward model and easy to use than the hierarchical and network database models. 
  • Any changes in tuples are manageable. The relational database model is also one of the most scalable models, as we can insert any information easily and quickly. 

Disadvantages:

  • The structure of the relational model is very straightforward and easy to understand. Users do not know how the data is stored. But, if a large amount of information is inserted, the database may slow down. 
  • Another disadvantage of using the relational model is hardware overhead. It requires sturdy and robust computer systems and storage devices. 

Apart from the hierarchical model, network model, entity-relationship model, and the relational model, several other data models are available. These data models are the object-oriented data model, object-relational data model, flat data model, associative data model, and context data model. 

In the object-oriented data model, all data entities and their relationships are treated in the form of objects. The flat data model is similar to the relational model. All information is represented in rows and columns of a table. An associative data model consists of entities and associations. Any independent object is called an entity and the connection between two independent entities is called association. 

Advantages and Disadvantages of Data Model

We discussed why we do data modeling. We know the reasons for representing the data in the form data model. This part of the article lets us move forward with the advantages and disadvantages of the data model. 

Advantages of the Data Model

  1. The data model acts as a blueprint for any software development process. Without any pre-planning, many software development processes fail and result in loss of time and money. However, the data model enables developers to find all possible software development strategies and find the best among them. 
  2. When developers use the data model, the cost of software development reduces. During the development process, the data model enables developers to find errors in the early stages. So, developers can fix those errors immediately. 
  3. The data model allows developers to build a well-organized database. It assists them in choosing the best approach for developing software databases. A well-organized database executes faster and produces results quickly.
  4. It reduces application errors and data errors. Also, the data model is responsible for managing risks to a greater extent. 
  5. The data model maps the information in a diagrammatic form, making it more manageable for developers to define primary keys, foreign keys, the relationship between entities, tables, etc.

Disadvantages of the Data Model

  1. The data model process is mapping the data in an understandable and diagrammatical form. Hence, the knowledge of the physical characteristics of the data is mandatory. 
  2. If any change is made in the data model structure, it must be reflected in the entire application or software being developed. 
  3. Data modeling requires very complex programming. 
  4. The database management system does not have a set manipulation language. 
  5. Data modeling is the navigational system. Hence, it requires more advanced knowledge and skills in software development and management processes. 

Conclusion

The data model is a diagrammatical or visual representation of data that is very easy to understand and interpret. Data modeling implies designing data models that store information in the database. The data model is designed at three different stages, the conceptual phase, the logical phase, and the physical phase. We have covered each stage of the data models in detail. 

Later, we discussed four different types of the data model, hierarchical model, network model, entity-relationship model, and the relational model. Each model is described in detail, along with their advantages and disadvantages. Lastly, we covered the pros and cons of data modeling. 

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