Please it is out of 20 mark give me the correct answer it should be 10 page
Distributed Database Concept
Introduction , Component of DDBMS and Differentiate b/n Distributed Database & Distributed Processing
Distributed Database Concept (4 marks):
- A distributed database is a collection of multiple interconnected databases that are geographically dispersed and function as a single database system.
- It allows users to access and manipulate data as if it were stored in a single location.
Components of DDBMS (6 marks):
1. Data Fragmentation: The process of dividing a database into smaller subsets called fragments. Each fragment can be stored at different locations in the distributed system.
2. Data Replication: The practice of storing multiple copies of the same data at different locations to improve data availability and performance.
3. Data Allocation: The process of determining where to store the data fragments or replicas in the distributed system.
4. Data Communication: The mechanisms and protocols used for data transfer and exchange between different sites in the distributed system.
5. Data Access Control: The security measures and mechanisms in place to control and restrict access to the distributed database.
6. Distributed Query Processing: The process of executing queries that involve data from multiple sites in the distributed system.
Differences between Distributed Database and Distributed Processing (10 marks):
1. Definition: A distributed database is a collection of geographically dispersed databases that function as a single logical database, while distributed processing refers to the distribution of processing tasks across multiple computers or nodes in a network.
2. Focus: Distributed databases focus on storing and managing data across multiple sites, whereas distributed processing focuses on distributing computational tasks and processing power.
3. Data Management: Distributed databases provide mechanisms for data fragmentation, replication, allocation, and access control to manage data across multiple sites. Distributed processing focuses on dividing tasks and processing workload among multiple nodes.
4. Dependencies: Distributed databases usually involve dependencies between data at different sites, where consistency and integrity are key concerns. Distributed processing can be done independently and tasks may not rely on specific data dependencies.
5. Data Coherency: In distributed databases, maintaining data coherency across multiple sites is crucial. Distributed processing may not necessarily require data coherency as tasks can operate independently.
6. Communication: Distributed databases require data communication between sites for data transfer, synchronization, and coordination. Distributed processing primarily involves message passing and communication for task distribution and coordination.
7. Performance: Distributed databases aim to improve data availability, reliability, and performance. Distributed processing aims to increase overall computational speed and efficiency by distributing tasks.
8. Applications: Distributed databases are commonly used in large-scale applications where data needs to be accessed and updated from multiple locations. Distributed processing is used in parallel computing, distributed systems, and networking applications.
9. Scalability: Distributed databases can scale horizontally by adding more sites or vertically by increasing the capacity of existing sites. Distributed processing can scale by adding more nodes or computers for increased processing power.
10. Complexity: Distributed databases are generally more complex due to the need for data synchronization, replication, and consistency management. Distributed processing focuses on task distribution and coordination, which can be comparatively simpler.