Architecture-of-Distributed-Database-Management-Systems (1) (1).pptx

Yudjgcgjfkhchkv

1. Presented By : Arnab Maitra 1st Year M.Tech CSE (AIML) NITTTR Kolkata Architecture of Distributed Database Management Systems

2. Introduction to Distributed DBMS A distributed database management system (DDBMS) stores and manages data across multiple computers. Importance Enables scalability, reliability, and availability in modern data management. Transparency Levels Ranges from complete data distribution transparency to local data access.

3. Standardization in DBMS ensures interoperability and data exchange. Distributed Systems Standardization applies to distributed systems, facilitating communication and data management. DNS and machine learning Standardization in DDBMS ANSI/SPARC Reference architecture for DBMS, defining three levels: external, conceptual, and internal.

4. Architectural Models for Distributed DBMSs Peer-to-Peer Nodes act as both clients and servers, exchanging data directly. Client/Server A centralized server manages data, while clients access it. Multidatabase Combines multiple autonomous DBMSs, offering a unified view.

5. Client / Server Systems A central server stores and manages data, while clients request and receive data. Server Role Initiates requests for data, processing received data for user presentation. Client Role Processes data requests, handles data storage and manages database resources.

6. Nodes in a peer-to-peer system act as both clients and servers. Peer-to-Peer Distributed DBMS Data Sharing Nodes can share data with others, promoting collaboration. No Central Authority Each node manages its data and interacts directly with other nodes. Each node in a peer-to-peer system acts independently, managing its own data and interacting with other peers as needed. Distributed Processing Query processing is distributed across multiple nodes, utilizing the combined computing power. High Fault Tolerance If one node fails, other nodes can continue processing requests, ensuring system uptime.

7. Combines multiple independent DBMSs into a single system. Interoperability Ensures seamless data exchange and communication between systems. Multidatabase Systems Autonomy Preserves the independence of individual databases. Heterogeneity Handles differences in data models, schemas, and database systems.

8. Data is organized logically into local and global schemas. Local Schema Represents data stored at a specific site. Global Schema Provides a unified view of all data across the system. Data Organization in Distributed DBMS

9. Components of a Distributed DBMS Key components that facilitate distributed database management. User Processor Provides user interfaces, processing transactions and queries. Storage Processor Manages data storage, access, and retrieval. Global Directory Maintains information about data location and distribution.

10. Conclusion Distributed DBMS architecture enables efficient data management in modern systems. Key Points DDBMSs enhance scalability, reliability, and availability. 2) Future Trends Cloud-based DDBMS, NoSQL, and data analytics integration.

11. THANK YOU