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Data Abstraction in DBMS – Different Levels Explained

Imagine a realm where complexities dissolve, and information seamlessly flows. Data abstraction functions akin to a magician’s tool, masking the complexities of data storage and retrieval while providing users with a simplified interface. It resembles peering through a keyhole into a vault of information, enabling users to access precisely what they require without delving into technical intricacies. 


From the raw data residing in the physical layer to the definition of relationships and structures in the logical layer and finally, to the effortless user interaction in the conceptual layer, abstraction empowers it all. Thanks to Data Abstraction in DBMS, navigating the vast landscape of data transforms into an enjoyable expedition, granting users a glimpse into boundless possibilities.


Table of Contents:



What is Data Abstraction?


Data abstraction involves concealing unnecessary or unrelated specifics from users and presenting a simplified perspective to facilitate data accessibility. Database systems comprise intricate data structures and relationships concealed to streamline user interaction. By abstracting data, only pertinent information is exposed to users, enhancing usability while maintaining data security. This practice ensures that users can interact with the database efficiently without being overwhelmed by its complexity.

What is Data Abstraction in DBMS?


In database management systems (DBMS), data abstractions entail concealing extraneous data from end-users. Complex data structures and relationships within database systems are abstracted to simplify user interaction. Through data abstraction, users access only the pertinent portions of the database, enhancing usability. 


Consider the example of email communication via Gmail: despite the intricate data storage mechanisms employed, users are primarily concerned with accessing and managing their emails. The specifics of data location, such as the physical storage location or data model utilised, are abstracted away from users, as they are not essential for email management. Thus, users interact with Gmail without needing to know the intricate details of its underlying data storage and management processes.


Read About: Difference Between DBMS and RDBMS

Different Levels of Data Abstraction in DBMS.


There are three levels of data abstraction in a DBMS, as mentioned below.


  • Physical or Internal Level
  • Logical or Conceptual Level
  • View or External Level


So, let’s look at each level in detail. First, we will discuss the physical level.


Physical or Internal Level


The levels of abstraction within a Database Management System (DBMS) offer various advantages to users and applications. The physical level, the lowest tier of data abstraction, delineates the storage mechanisms employed within the database. It encompasses data structures utilised for storage and the methods employed to access stored data. 


Due to its complexity, this level is typically obscured from users. Database administrators hold the responsibility of determining the specifics of data storage within the database, encompassing aspects such as data organisation, allocation of disk space, and methods for data access.


Logical or Conceptual Level


The logical level, situated between the physical and conceptual tiers, delineates the data housed within the database and the relationships existing among them. This level is relatively less intricate when contrasted with the physical tier. Programmers typically operate within this realm, where decisions regarding data structures, table organisation, relationships, and constraints are made based on the nature of the data.


View or External Level


At the pinnacle of abstraction lies the conceptual level. Within this tier, various views are established, with each offering a partial representation of the complete dataset tailored to the user’s needs. This level defines multiple views of the database to simplify user interaction, providing a comprehensible overview of the data. As the highest tier of abstraction, the conceptual level is designed to be the most user-friendly and easily understandable.


Advantages of Data Abstraction in DBMS


The abstraction levels within a Database Management System (DBMS) offer various advantages to users and applications:


  • Separation of Data Presentation and Storage


    DBMS abstraction levels establish a distinction between how data appears to users or applications and how it’s stored and retrieved by the system. This separation enables modifications to be made to the physical storage and access methods without impacting the external or conceptual levels.


  • Simplified Database Management


    DBMS abstraction levels facilitate database administrators in managing the database effectively. They can implement changes to the physical storage and access methods without disrupting users or applications interacting with the database.


  • Performance Optimization


    DBMS abstraction levels empower the system to optimise physical storage and access methods for enhanced performance, all while preserving the user or application perspective of the data.


  • Meaningful Data Representation


    DBMS abstraction levels enable users or applications to perceive data in a meaningful manner without concerning themselves with the underlying implementation details. This flexibility makes it simpler to adapt to evolving business requirements and user preferences over time.



Disadvantages of Data Abstraction in DBMS


Below are some drawbacks of data abstraction:


  • Complexity for Developers


    Data abstraction can be perplexing for developers due to the intricacies present at multiple levels within the database.



  • Increased Navigation Complexity


    Adding an additional layer to the code can make navigation challenging, potentially leading to difficulties in accessing and manipulating data.



  • Limitations on Modifying DBMS Behavior


    Implementing changes to the behaviour of the Database Management System (DBMS) at lower abstraction levels may pose a daunting task or even be rendered impossible due to the abstraction in place.



To Cut It Short


Data abstraction within DBMS serves a vital function in simplifying the intricacies linked to data storage and retrieval, leading to enhanced usability and efficiency. By concealing unnecessary particulars and furnishing users with a streamlined interface, data abstraction facilitates seamless interaction with databases. It fosters efficient database management, amplifies performance optimisation, and streamlines adaptation to evolving business requisites.


Adopting data abstraction is essential for maximising the potential of database systems and navigating the expanding realm of data-driven decision-making. Take the next step in your data science journey by enrolling in the Advanced Certification Programme in Data Science & Analytics, powered by The University of Chicago Join the course in unlocking the transformative power of data analytics and shaping the future landscape of business intelligence.





Data abstraction allows users to concentrate on the fundamental operations of a machine, enabling them to utilise it according to their needs. Consider a television remote control, for instance. The buttons provide instructions for adjusting the volume and powering the television on and off, and these functionalities are pertinent to the user.
There are mainly 3 levels of data abstraction:  
  • Physical or Internal Level.
  • Logical or Conceptual Level.
  • View or External Level.
Within computer science, an abstract data type (ADT) refers to a mathematical construct representing data types based on their user-oriented behaviour. It defines the semantics of the data from the user's perspective, including the range of potential values, permissible operations on data of this type, and the expected behaviour of these operations.

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