What is Database Design? And It’s Important

What is Database Design? And It’s Important

What is Database Design? I may have used the term “database” frequently. This phrase places a lot of attention on its arms. It is commonly utilized with communities or people outside of the IT industry and is not solely tense to the developer’s perspective. Technically speaking, the term “database” is more of a storage phrase used to describe the relationship with many types of data that consolidate in one location. So, a database is an ordered collection of data typically stored and retrieved electronically by computer systems. This article heavily focuses on database architecture and considers its connections to citeable terminology and approaches. To comprehend the details, we’ll discuss those concepts concerning database design.

What is Database Design?

An enterprise data management system’s design, development, implementation, and maintenance can improve with the help of a group of actions or procedures collectively known as database design. The cost-effective methods are significantly influenced in terms of disc storage space by adequately designing a database, which also improves data consistency while lowering maintenance costs. Consequently, creating a database must be done with clever thought. The designer should adhere to the limitations when deciding how the pieces relate to one another and what must save data types.

Important Factors to Consider When Designing a Database - Kingfisher  Technologies

The primary goals of database design are to create logical and physical representations of the suggested database system. To describe, it should notice that the analytic model focuses primarily on the data requirements. Because of this, monolithic considerations must, which means that must store physical data independently of physical conditions. In contrast, the physical database design model translates the logical design model of the database by maintaining control over physical media using hardware resources and software programmes like Database Management System (DBMS).

Why is Database Design meaningful?

The following points provide below to help clarify the crucial factor that should consider when highlighting the significance of database design.

  • Database designs provide the blueprints for how data will be stored in a system. Therefore, the database’s structure significantly influences the total performance of any programme.
  • The designing guidelines established for a database provide a clear understanding of how any application will behave and how it will handle requests.
  • A proper database design satisfies all user requirements, which is another reason to stress the database design.
  • Last but not least, effectively implementing the limitations of developing a highly efficient database can significantly shorten the processing time of an application.

Life Cycle

As we are concentrating on database architecture, the life cycle of a database is not a crucial issue that needs to be advanced in this article. However, it is essential to comprehend the whole workflow and lifecycle of the database before moving on to the designing models that make up database design.

Requirement Analysis

Before proceeding with database design, one must complete planning regarding the project’s fundamental requirements. They can therefore described as:

Planning – The DDLC planning during this step (Database Development Life Cycle). Before moving further, the strategic factors take into account.

System definition – Following the planning, this stage covers the parameters and purviews of the appropriate database.

Database Designing

The next stage is to divide the user-based needs into different models while creating the database to prevent imposing a significant load or dependence on a single feature. As a result, there has been some model-centric thinking, and logical and physical models are essential in that context.

Physical Model: The physical model is concerned with how the logical model is used and implemented.

Logical Model: The main goal of this stage is to create a model based on the suggested needs. The entire model makes on paper without considering implementation or DBMS adoption.


The final step deals with implementing strategies and verifying that behaviour matches our specifications. Continuous integration testing of the database using various data sets and converting the data into machine-understandable language are used to ensure it. Most data manipulation occurs during these steps, where queries conduct to determine whether or not the application builts satisfactorily.

Data loading and conversion – This section imported and converted data from the old system to the new one.

Testing – At this stage, finding errors in the recently put-into-place system is the primary goal. However, testing is essential due to its direct database check and requirement specification comparison.

Database Design Process

Several different conceptual approaches must be kept in mind when developing a database. First, a perfect database design that is well-structured must be able to:

  • It can save disk space by removing redundant data.
  • It Protects the accuracy and integrity of the data.
  • Access to data in relevant ways provides.
  • Physical and logical data models comparison.

Database Design Process - YouTube


Without having to worry about the physical implementations in the database, a logical data model typically provides the most specific description of the data feasible. For example, an analytical data model may include the following features:

  • All of the entities and their connections.
  • Each entity contains attributes that are clearly defined.
  • Each entity’s primary key identify.
  • Foreign keys used to show a link between various entities are listed.
  • At this point, normalization takes place.

The following method uses to create a logical model:

  • Provide primary keys for each entity.
  • Give details about the simultaneous relationships between several things.
  • List the characteristics of each entity.
  • Put an end to many-to-many relationships.
  • Normalization that it carried out.

Additionally, it’s crucial to critically assess the design in light of requirement collection after using the method above. There is a potential to create a highly effective database architecture that uses the native approach if the procedures above rigorously follow.

See the illustration below for a detailed explanation of these concepts.

A logical data model has primary keys for each attribute, unlike a conceptual data model, which lacks primary keys for any of its features. It compares the analytical data model depicted in the above picture with some sample data in the diagram. Additionally, a logical data model establishes associations between various entities by allowing for the use of foreign keys.


A physical data mode typically represents the approach or notion of designing the database. The fundamental goal of the physical data model is to display all aspects of a table’s structure, including the column name, column data type, constraints, primary and foreign keys, and the relationships between different tables. A physical data model has the following characteristics:

  • It Specifies all the tables and columns.
  • Specifies the foreign keys, which often describe the connection between tables.
  • De-normalization could happen depending on user requirements.
  • There will be more apparent grounds for difference than a logical model because of the physical factor taken into account.
  • For various RDBMS, physical models could differ. For instance, SQL Server and MySQL could have different data type columns.

The following factors are taken into account while building a physical data model:

  • Make tables out of the entities.
  • Should create foreign keys from the defined relationships.
  • Make columns out of the data attributes.
  • Adapt the data model limitations to the physical needs.

We might distinguish that entity names are treated as table names in a physical database and attributes are treated as column names by comparing this physical data model to the prior logical model. Additionally, based on the actual database used, the data types for each column are described in the physical model.


Entity: An entity in a database is any abstract data we store—a customer, for instance, or goods.

Attributes- An attribute is a specific type of data that includes elements such as length, name, price, etc.

Relationship – A relationship is a link between two entities or figures. One can connect with different family members, for instance.

Foreign key: It is a link to another table’s Primary Key. A foreign key contains columns with values that are unique to the primary key column they refer to process.

Primary key -A primary key is a record pointer used to identify properties of a database in a way that is both unique and non-null.

Normalization: A flexible data model must adhere to specific rules. Normalizing is the process of putting these standards into practice.


A way of determining the opportunities and gaps for building a suitable usage method is database design. The fundamental part of a system, it provides a blueprint for the data and how it behaves within the system. Due to the overly high user expectations, adhering to the standard practises of database design may only provide a slim probability of achieving the desired efficiency. The several design models that depict the perfect database design were also taught to us separately, along with an endless discussion of their characteristics and applications. Additionally, we discovered that a database’s life cycle determines its design, How the lifecycle of a database defines the creation of the database and how to include the design concept into lifecycle techniques so that practical and highly sophisticated database designs based on user requirements.

Also read: What is a Database? Uses, Types, and Many More

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