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Physical Data Model: What You Need to Know

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A physical data model is a framework or schema that describes how data is actually kept in a database.

Before getting started with the physical data model, let’s understand what Data Modeling is all about.

What characteristics enable users to use a database effectively? How could you be certain that the database you’re developing will satisfy all of your requirements? Think of the data modeling concept as the answer to getting data and converting it into a database that can be used.

What is Data Modeling?


Data Modeling is the process of generating a streamlined representation of a software application and the data components it includes, using text and symbols to depict the information and how it streams.

It is a technique for outlining and visualizing all the different spots where a software program or application keeps data and how these different sources of information will integrate and stream into each other.

Data modeling is an important aspect of data management. It assists in the identification of information requirements for workflows by giving a visual representation of data points and their behavioral pattern.

It allows people to determine and understand how data will be managed, modified, viewed, and distributed throughout an organization.

Importance of Data Modeling

Modern organizations gather a lot of data from numerous sources. To make effective strategic decisions, you must examine data for practical insights.

Effective data collection, storage, and computation are necessary for accurate data analysis. Numerous tools are used for efficient analysis depending on the data type, such as structured, semi-structured, ordinal, etc.


Data modeling allows you to comprehend your data and select the best solution to manage and control it. Companies create a data model before they develop database systems for their operations, just as an architect creates a plan before building a house.

The main advantages of data modeling are as follows:

  • Provides quick and effective solutions for database design process and deployment.
  • promotes uniformity in data reporting and development work across the company

And also, implementing a data modeling concept facilitates the interaction between the analytics teams and the database engineers.

Types of Data Modeling

Three distinct types of data models are used by data modelers to describe marketing concepts and procedures, pertinent data items and their attributes & relationships, and practical data management frameworks.

As companies create functional programs and databases, the data models are often developed step-by-step. Here are the various data model kinds and what each one entails:

#1. Conceptual Data Modeling

It is basically an organized view or a visual representation of database concepts and their relationships. It serves as the conventional beginning point for data modeling, defining the various data sources and data flow inside the organization.

It serves as a high-level guideline for creating logical and physical models and is a vital component of the documentary evidence for the data architecture.


The conceptual data model just depicts the overall layout and content, not the specifics of each object. Your company’s entire organizational structure and data are described by a conceptual data model.

It is used to organize business concepts that your data engineers have specified. It focuses on designing entities, defining an entity’s attributes, and defining relationships between objects instead of just the specifics of the database structure.

For example, you might have data on stores, time, and products. These sets of data, or entities, all have connections to other entities. In this conceptual data model, both the entities and the entity connections are specified.

#2. Logical Data Modeling

A logical data model expands on the conceptual model with precise content properties within every entity and detailed connections between these attributes. The creation of a low-complex logical data model can be done using the conceptual data model as a guideline.


The relationships between data items are displayed in logical data models, which also give a technical description of the data. For example, Customer A purchases Item B from Store C.

The arrangement of the data objects and the connections between them are further defined by this model. Since the goal is to create a detailed diagram of standards and data structures, logical data models are typically employed for a particular project.

The logical data model provides more information about the conceptual model’s entire design, but it neglects details about the database itself because the model can be utilized to describe various database products and services.

This acts as a technical model of the principles and data structures as established by data engineers, and it helps in decision-making on the physical data model needed to meet your operational and data requirements.

#3. Physical Data Modeling

Generally, the Implementation of a data model in a database is described by a physical data model. Physical data models are used by database engineers to develop layouts and architecture for databases.

By simulating the RDBMS components, including tables, fields, indexes, column keys, constraints, triggers, and others, the physical data model greatly assists in visualizing the design of databases.


It specifies the organizational processes that a database or file system utilizes to capture and handle the data. The physical data model explains the relevant details of how the logical model will be implemented.

It provides database abstraction and aids in creating the schema or a layout. This is due to the extensive meta-data that a physical data model provides.

This article will primarily discuss the concept of physical data modeling.

Let’s get started!

What is a Physical Data Model?

A physical data model is a framework or architecture that describes how data is actually kept in a database. The actual schema of a database is designed using this physical data model. This comprises all of the multiple tables, their columns, and the connections between them.

The internal schema of a database is designed using a physical model. The goal is to put the database into use. This physical model can be directly converted into actual database design, supporting the further evolution of information management. When using several database systems, it is possible for different physical models to be created from the same logical data model.

Characteristics of a physical data model

  • It covers the data requirements for a specific project or program, although it might be combined with some other physical models depending on the project’s objectives.
  • The specific datatypes & allocated sizes, and default values for columns should be specified.
  • Views (virtual table based on the result-set), indexes, transactions, and other concepts are defined, including primary and foreign keys.

Database engineers build the physical data model before applying the final database schema into operation. To ensure that every architecture component has been considered, they also use comprehensive data modeling approaches.

Steps needed to design a physical data model

These are the steps to be followed to create a physical data model.

  • Build a physical data model using the logical data model that is already in existence.
  • To the physical data model, add database attributes and properties.
  • Convert entities into tables and entity relationships into foreign keys.
  • Convert attributes into columns.
  • Verify that everything is in order by comparing the database and data model.
  • If there are any changes between the current and earlier iterations of the data model, generate a change log record.

Physical vs. Conceptual vs. Logical Data Model

Here, we compare these three different categories of data models. The various features are contrasted in the table below.

FeatureConceptualLogical Physical
Entity Names
Entity Relationships
Primary Keys
Foreign Keys
Table Names
Column Names
Column Data Types

Entities and connections are represented in a conceptual data model. The characteristics and primary key are not mentioned. It merely covers the high-level design, including which tables should exist and their linkages.

Following the conceptual model, the logical model is created. The relationships between data items are displayed in logical data models, which also give a technical description of the data. Additionally, there is a physical data model that expands on the logical data model and allocates each field with its data type, size, etc.

Data Modelling Learning Resources


You can find many resources online to help you understand data modeling, but it might be difficult to pick the good ones. Data modeling is a valuable talent to have, but it has to be learned in the right ways.

And if you’re trying to improve your data management or analytical skills for personal or business purposes, have a look at this list of the top data modeling courses and books.

#1. Mastering Data Modeling Fundamentals

Through this Udemy course, You can Learn the methods required to create data models for your organization containing entities, characteristics, associations, structures, and other modeling elements that are semantically accurate.


Learners just require a fundamental understanding of data management terms and structures, such as RDBMS tables and how various data sets conceptually relate to each other.

#2. Advanced-Data Modeling


This Coursera course is fantastic for those looking to advance their careers. By the end of this course, you will have a solid understanding of how to use fundamental data modeling techniques and traverse contemporary storage solutions for a database system. No prior knowledge of database engineering is required for learners.

#3. OBIEE 12c Data Modeling Course

This Udemy course is for anyone interested in a career in OBIEE Data Modeling, including students, IT professionals, and project managers.


By the end of this course, you will be able to implement several time series functions and data modeling concepts, including data denormalization, Dimensional data modeling, and star schema modeling.

#4. Excel Business Intelligence: Data Modeling 101

In this LinkedIn course, The trainer covers the fundamentals of database architecture and normalization, walks through Excel’s data model interface, and provides tested techniques.


You can improve your knowledge of table linkages, topologies, and other concepts by studying the topics presented in this course. No prerequisites are required to start with this course.

#5. The Data Warehouse Toolkit

In this book, the authors introduce students to dimensional modeling approaches such as invoicing, customer interactions, and basic database building. Also, the new and improved star schema dimensional modeling patterns are discussed.

Additionally, this book includes guidelines for advanced simulation meetings with corporate stakeholders. No prior knowledge of data modeling is required. Even beginners feel comfortable learning data modeling concepts by reading this book.

#6. Data Modeling Made Simple, 2nd Edition

This book is written in a conversational manner that inspires users to learn the key objectives, such as understanding when a data model is required as well as which form will be most beneficial, creating a normalized relational database system, using methods to transform a data model into such an impactful physical layout for the database.

This book offers a realistic functional understanding of data modeling principles and best practices for business or IT purposes.

#7. Data Modeling Essentials, Third Edition

This book teaches the fundamentals of data modeling while emphasizing technique development rather than only becoming knowledgeable with “the principles.”

Preview Product Rating Price
Data Modeling Essentials, Third Edition Data Modeling Essentials, Third Edition $81.95

This book examines the complexities of creating systems in real-world circumstances by weighing the pros and cons of various alternatives and utilizing language and graphical analysis techniques that reflect industry standards. This encourages learners to apply the fundamentals of data modeling to actual models.


Organizations and companies are continuously attempting to win over customers, and to do so, they must establish tactics that will propel their services forward. These tactics entail the use of data models to improve business operations.

A good data model will help you save money and time and enhances productivity. Utilizing the data modeling concept can ensure that a company can become competitive by implementing adjustments based on the data it has gathered.

There is a great demand for professionals with data modeling skills these days. A job in this industry might offer numerous opportunities because data is always available for study and storage. I hope you found this article helpful in learning data modeling-related concepts.

You may also be interested in learning about SQL Triggers.

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