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A distributed database is a collection of multiple, interconnected databases spread across different locations, managed by a database management system (DBMS) to appear as a single, unified database. It leverages computer networks to store, process, and retrieve data across geographically dispersed nodes, improving scalability, reliability, and performance. Below is an overview of distributed databases using computer networks:

Key Concepts
Definition: A distributed database stores data across multiple computers (nodes) connected via a network, such as LAN, WAN, or the internet. Each node may have its own DBMS, but they work together to provide a cohesive view of the data.
Types of Distributed Databases:
Homogeneous: All nodes use the same DBMS software and schema.
Heterogeneous: Nodes may use different DBMS software or schemas, requiring middleware for integration.
Data Distribution:
Fragmentation: Data is divided into subsets (fragments) stored on different nodes (e.g., horizontal or vertical partitioning).
Replication: Copies of data are stored on multiple nodes for redundancy and faster access.
Hybrid: Combines fragmentation and replication.
Network Dependency: Nodes communicate over computer networks (e.g., TCP/IP, HTTP) to execute queries, synchronize data, and maintain consistency.
Architecture
Components:
Nodes: Individual computers or servers hosting part of the database.
DBMS: Software at each node managing local data (e.g., MySQL, PostgreSQL, MongoDB).
Global DBMS: Coordinates operations across nodes, ensuring transparency to users.
Communication Network: Facilitates data exchange and query processing.
Transparency:
Location Transparency: Users don’t need to know where data is stored.
Fragmentation Transparency: Users are unaware of how data is split.
Replication Transparency: Users don’t see data copies.
Models:
Client-Server: Clients send requests to a central server managing the distributed database.
Peer-to-Peer: Nodes act as equals, sharing responsibilities.
Federated: Autonomous databases integrate to form a distributed system.
Advantages
Scalability: Add nodes to handle more data or users.
Reliability: Data replication ensures availability despite node failures.
Performance: Local data access reduces latency for geographically distributed users.
Flexibility: Supports diverse applications, from IoT to global enterprises.
Data Locality: Data is stored closer to where it’s used, improving efficiency.