This document introduces distributed database management systems (DDBMS), highlighting their importance for scalability, reliability, and availability. It discusses various architectural models such as peer-to-peer and client/server systems, emphasizing the roles of nodes and data management processes. Additionally, it notes future trends in DDBMS, including cloud integration and the use of NoSQL databases.

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.
1) Key Points
DDBMSs enhance scalability,
reliability, and availability.
2) Future Trends
Cloud-based DDBMS, NoSQL, and
data analytics integration.

11. THANK
YOU