Describe Drawbacks of Relational DBMS.
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.
Drawbacks of Relational DBMS (Database Management System) include:
1. Complexity: Setting up and managing a relational DBMS can be complex, especially for large databases. It requires expertise in database design, normalization, and query optimization.
2. Performance limitations: Relational databases may face performance issues when dealing with large datasets or complex queries. Joining multiple tables and executing complex queries can result in slower response times.
3. Scalability challenges: Scaling a relational DBMS can be challenging. As the size of the database or the number of users increases, performance may degrade unless proper database performance tuning techniques are implemented.
4. Lack of flexibility: Relational DBMSs often require a predefined schema and strict data structure, making it difficult to accommodate changes or modifications in the database structure or data types.
5. Limited support for unstructured data: Relational DBMSs are primarily designed to handle structured data. They may struggle to efficiently store or query unstructured or semi-structured data such as text documents, images, videos, or JSON.
6. Cost: Implementing and maintaining a relational database system can be expensive. Licensing fees, hardware requirements, and expertise needed to manage the system can contribute to the overall cost.
7. Data integrity constraints: Although data integrity constraints are essential for maintaining data consistency, they can sometimes hinder flexibility and performance. Enforcing constraints like foreign keys or unique constraints can slow down operations.
8. Concurrency issues: Relational databases that have multiple users accessing and modifying data simultaneously may experience concurrency issues like deadlocks or inconsistent data states, requiring careful management and transaction control.
9. Limited support for distributed systems: Traditional relational databases are not inherently designed for distributed and decentralized systems, making it challenging to implement distributed architectures or scale-out solutions.
10. Learning curve: Becoming proficient in using and managing relational DBMSs requires significant time and effort. Users may need to learn SQL (Structured Query Language) and understand the underlying database concepts to effectively use these systems.