View of Data in DBMS

View of data in DBMS narrate how the data is visualized at each level of data abstraction? Data abstraction allow developers to keep complex data structures away from the users. The developers achieve this by hiding the complex data structures through levels of abstraction.

There is one more feature that should be kept in mind i.e. the data independence. While changing the data schema at one level of the database must not modify the data schema at the next level. In this section, we will discuss the view of data in DBMS with data abstraction, data independence, data schema in detail.

Content: View of Data in DBMS

1. Data Abstraction
2. Data Independence
3. Instance and Schema
4. Key Takeaways

Data Abstraction

Data abstraction is hiding the complex data structure in order to simplify the user’s interface of the system. It is done because many of the users interacting with the database system are not that much computer trained to understand the complex data structures of the database system.

To achieve data abstraction, we will discuss a Three-Schema architecture which abstracts the database at three levels discussed below:

 Three-Schema Architecture:

The main objective of this architecture is to have an effective separation between the user interface and the physical database. So, the user never has to be concerned regarding the internal storage of the database and it has a simplified interaction with the database system.

The three-schema architecture defines the view of data at three levels:

1. Physical level (internal level)
2. Logical level (conceptual level)
3. View level (external level)

1. Physical Level/ Internal Level

The physical or the internal level schema describes how the data is stored in the hardware. It also describes how the data can be accessed. The physical level shows the data abstraction at the lowest level and it has complex data structures. Only the database administrator operates at this level.

2. Logical Level/ Conceptual Level

It is a level above the physical level. Here, the data is stored in the form of the entity set, entities, their data types, the relationship among the entity sets, user operations performed to retrieve or modify the data and certain constraints on the data. Well adding constraints to the view of data adds the security. As users are restricted to access some particular parts of the database.

It is the developer and database administrator who operates at the logical or the conceptual level.

3. View Level/ User level/ External level 

It is the highest level of data abstraction and exhibits only a part of the whole database. It exhibits the data in which the user is interested. The view level can describe many views of the same data. Here, the user retrieves the information using different application from the database.

The figure below describes the three-schema architecture of the database:

In the figure above you can clearly distinguish between the three levels of abstraction. To understand it more clearly let us take an example:

We have to create a database of a college. Now, what entity sets would be involved? Student, Lecturer, Department, Course and so on…

Now, the entity sets Student, Lecturer, Department, Course will be stored in the storage as the consecutive blocks of the memory location. This is the physical or internal level and is hidden from the programmers but the database administrator is it aware of it.

At the logical level, the programmers define the entity sets and relationship among these entity sets using a programming language like SQL. So, the programmers work at the logical level and even the database administrator also operates at this level.

At the view level, the users have the set of applications which they use to retrieve the data they are interested in.

Data Independence

Data independence defines the extent to which the data schema can be changed at one level without modifying the data schema at the next level. Data independence can be classified as shown below:

Logical Data Independence:

Logical data independence describes the degree up to which the logical or conceptual schema can be changed without modifying the external schema. Now, a question arises what is the need to change the data schema at a logical or conceptual level?

Well, the changes to data schema at the logical level are made either to enlarge or reduce the database by adding or deleting more entities, entity sets, or changing the constraints on data.

Physical Data Independence:

Physical data independence defines the extent up to which the data schema can be changed at the physical or internal level without modifying the data schema at logical and view level.

Well, the physical schema is changed if we add additional storage to the system or we reorganize some files to enhance the retrieval speed of the records.

Instances and Schemas

What is an instance?

We can define an instance as the information stored in the database at a particular point of time. Let us discuss it with the help of an example.

As we discussed above the database comprises of several entity sets and the relationship between them. Now, the data in the database keeps on changing with time. As we keep inserting or deleting the data to and from the database.

Now, at a particular time if we retrieve any information from the database then that corresponds to an instance.

What is schema?

Whenever we talk about the database the developers have to deal with the definition of database and the data in the database.

The definition of a database comprises of the description of what data it would contain what would be the relationship between the data. This definition is the database schema.

So that’s all about the view of data in the database which help us to understand the database from users, developers and database administrator aspects.