What distinguishes distributed databases from centralized ones?

Distributed databases are distinguished from centralized ones by their data storage across multiple physical locations.

In a centralized database, all data is stored and managed in a single location, typically on a central server. This server is responsible for processing data requests, which can lead to a bottleneck if the server becomes overloaded. Centralised databases are easier to manage due to their single location, but they also present a single point of failure. If the central server goes down, the entire database becomes inaccessible.

On the other hand, a distributed database system stores and manages data across multiple physical locations, which could be spread across different buildings, cities, or even countries. This distribution of data can be done in various ways, such as partitioning the data (each location stores a portion of the data) or replication (each location stores a copy of the entire database).

Distributed databases offer several advantages over centralized ones. Firstly, they provide improved reliability and fault tolerance. If one site fails, the system can continue to operate by accessing data from other sites. Secondly, they offer better performance as data is located closer to the users, reducing data access time. Thirdly, they can easily scale to accommodate larger amounts of data by simply adding more sites.

However, distributed databases also come with their own set of challenges. They are more complex to manage due to the need to synchronise data across multiple sites, which can lead to issues such as data inconsistency. Also, the cost of setting up and maintaining a distributed database system can be higher than a centralized one due to the need for multiple servers and network infrastructure.

In conclusion, the main distinction between distributed and centralized databases lies in their data storage and management. While centralized databases store all data in a single location, distributed databases spread data across multiple locations, offering improved reliability, performance, and scalability at the cost of increased complexity and potential data inconsistency.