What are the key differences between distributed databases and cloud databases?

Distributed databases and cloud databases differ primarily in their architecture and deployment models. A distributed database stores data across multiple physical locations, which could be servers in the same data center or spread globally. This design emphasizes scalability, fault tolerance, and low latency for geographically dispersed users. Examples include Apache Cassandra, which partitions data across nodes, and Google Spanner, which synchronizes data across regions using atomic clocks. In contrast, a cloud database refers to any database hosted and managed within a cloud provider’s infrastructure (e.g., AWS, Azure). These can range from traditional relational databases like Amazon RDS (MySQL) to fully managed services like Azure Cosmos DB. The key distinction is that “cloud database” defines where and how the database is hosted, while “distributed database” describes how data is organized across nodes.

Use cases also differ. Distributed databases excel in scenarios requiring high availability and resilience to regional outages. For example, a global e-commerce platform might use Cassandra to ensure orders are processed even if one data center fails. Cloud databases, however, focus on reducing operational overhead. Services like Amazon Aurora automate backups, scaling, and patching, letting developers focus on application logic rather than infrastructure. While some cloud databases (e.g., Cosmos DB) are also distributed, not all cloud databases require distributed architecture. A single-node PostgreSQL instance running on a cloud VM is a cloud database but not distributed.

Operationally, distributed databases demand expertise in consistency models and network latency. Developers must choose between strong consistency (like Spanner) or eventual consistency (like Cassandra) based on trade-offs between data accuracy and performance. Cloud databases abstract these complexities: scaling storage or compute is often handled via configuration settings or APIs. For example, DynamoDB automatically partitions data as workloads grow. However, cloud databases may introduce vendor lock-in or latency if not optimized for multi-region access. Distributed databases offer more control over data placement but require manual tuning for replication and failover. Both approaches address scalability but prioritize different aspects of manageability and architectural flexibility.