Describe Drawbacks of Relational DBMS.

Answer 1

1. Scalability: Relational database management systems (DBMS) can face scalability issues when handling large volumes of data. As the database grows in size, the performance of queries and transactions can deteriorate, leading to slower response times.

2. Complexity: Relational DBMS often have complex structures and require skilled administrators to design and manage them effectively. Setting up and maintaining a relational database can be time-consuming and requires a deep understanding of the underlying database schema.

3. Lack of flexibility: Relational DBMS follow a rigid schema, which makes it challenging to accommodate changes in data structure. Modifying the database schema usually involves downtime and can be disruptive to ongoing operations.

4. High costs: Relational DBMS are often expensive, both in terms of software licenses and hardware requirements. The need for specialized hardware and IT resources can significantly add to the overall cost of implementing and maintaining a relational database.

5. Performance limitations: Relational databases sometimes struggle to handle complex queries or large datasets efficiently. As relational DBMS are primarily designed for structured data, they may not perform as well when dealing with unstructured or semi-structured data.

6. Concurrency issues: Multiple users accessing and modifying the same data concurrently can lead to concurrency control challenges in relational DBMS. Unless handled correctly, this can result in data inconsistencies or conflicts.

7. Replication and synchronization challenges: Replicating and synchronizing data across multiple servers or databases can be complex and error-prone in a relational DBMS. Maintaining consistency and ensuring data integrity during the replication process can be challenging.

8. Lack of support for multimedia data: Relational DBMS are not well-suited for handling multimedia data types such as images, audio, and video. Storing and retrieving such data in a relational database can be cumbersome and may require external storage solutions.

9. Limited support for distributed computing: Traditional relational DBMS are often centralized, meaning they do not offer built-in support for distributed computing. Scaling out a relational database to distribute data across multiple servers can be a complicated process.

10. Data redundancy: Relational DBMS typically store data in multiple tables to ensure data normalization and maintain referential integrity. This can lead to data redundancy, increasing storage requirements and potentially impacting performance.