What is Data Independence in DBMS?
Data independence is a property of a database management system by which we can change the database schema at one level of the database system without changing the database schema at the next higher level. In this article, we will learn in full detail about data independence and will also see its types. If you read it completely, you will understand it easily.
What is Data Independence in DBMS?
In the context of a database management system, data independence is the feature that allows the schema of one layer of the database system to be changed without any impact on the schema of the next higher level of the database system. ” Through data independence, we can build an environment in which data is independent of all programs, and through the three schema architectures, data independence will be more understandable. Data via two card stencils along with centralized DBMS data is a form of transparency that has value for someone.
It can be summed up as a sort of immunity of user applications that adjusts correctly and does not change addresses, imparting the class of data and their order. I want the separate applications not to be forced to deal with data representation and storage specifics because this decreases quality and flexibility. DBMS permits you to see data with such a generalized sight. It actually means that the ability to change the structure of the lower-level schema without presenting the upper-level schema is called data independence.
Types of Data Independence
There are two types of data independence.
- logical data independence
- Physical data independence
Logical Data Independence
- Changing the logical schema (conceptual level) without changing the external schema (view level) is called logical data independence.
- It is used to keep the external schema separate from the logical schema.
- If we make any changes at the conceptual level of data, it does not affect the view level.
- This happens at the user interface level.
- For example, it is possible to add or delete new entities, attributes to the conceptual schema without making any changes to the external schema.
Physical Data Independence
- Making changes to the physical schema without changing the logical schema is called physical data independence.
- If we change the storage size of the database system server, it will not affect the conceptual structure of the database.
- It is used to keep the conceptual level separate from the internal level.
- This happens at the logical interface level.
- Example – Changing the location of the database from C drive to D drive.
Difference Between Physical and Logical Data Independence
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Physical Data Independence |
Logical Data Independence |
|---|---|
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It mainly concerns how the data is stored in the system. |
It mainly concerns about changes to the structure or data definition. |
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It is easier to achieve than logical independence. |
It is difficult to achieve compared to physical independence. |
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To make changes at the physical level we generally do not require changes at the application program level. |
To make changes at the logical level, we need to make changes at the application level. |
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It tells about the internal schema. |
It tells about the conceptual schema. |
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There may or may not be a need for changes to be made at the internal level to improve the structure. |
Whenever the logical structure of the database has to be changed, the changes made at the logical level are important. |
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Example- change in compression technology, hashing algorithm, storage device etc. |
Example – adding/modifying or deleting a new attribute. |
Conclusion
The data independence property of the database is an expected property that relies on separating the logical and physical aspects of storing and accessing data. This means that it is easy to make structural modifications to the database without affecting the applications that use it. This is a situation that impacts the capacity of the organization to remain adaptable in the dynamic business environment, as well as making sure that the technological advancements within the organization are interoperable over a long period of time.
Frequently Asked Questions on Data Independence – FAQs
Why is data independence important in database management systems?
Data independence is important since it means that modifications to the database schema at one level need not result in the same at higher levels cascading down. Scalability is a feature that allows an organization to modify its system resources without interrupting ongoing tasks when business needs change or technological changes occur.
How does logical data independence differ from physical data independence?
Logical data independence refers to designing systems where the logical schemas (conceptual level) can be changed without affecting the logical schemas (view level), while physical data independence involves designing systems where the physical schemas (storage level) can be changed without impacting the logical schemas. The first, logical data independence, looks into the schema or data definition, while the last, physical data independence, looks at the storage mechanism of the given system.
What are some examples of achieving physical data independence?
The achievement of physical data independence is demonstrated in the case of changing the backup and recovery paths (e.g., the storage location of the actual database) from one hard drive to another (e.g., C drive to D drive), a reformation for better compression techniques, upgrading the hashing algorithm, or a better storage device. These changes are the physical-level alterations that don’t disrupt the conceptual structure of the database.
How does data independence contribute to organizational adaptability?
Data independence assists in increasing flexibility in the organization by enabling the ease of changing data in the database. This flexibility not only requires businesses to have the ability to respond immediately to dynamic market conditions but also to integrate with new technologies and maintain interconnectivity over time. Data independence is achieved when data and their actions are divorced from application logic. And that provides a firm space for introducing new changes without affecting the business.
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