What is the difference between a distributed database and a traditional relational database?

A distributed database and a traditional relational database differ primarily in how they store and manage data across systems. A traditional relational database (RDBMS) operates on a single server, centralizing data storage and processing. It relies on structured tables with predefined schemas and uses SQL for queries, enforcing ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure transactional reliability. In contrast, a distributed database spreads data across multiple physical locations, such as servers or data centers, often using a network of nodes. This architecture allows data to be partitioned or replicated globally, prioritizing scalability and fault tolerance over strict consistency in some cases. For example, systems like MySQL or PostgreSQL represent traditional RDBMS, while distributed databases include Apache Cassandra or Amazon DynamoDB.

The key distinction lies in scalability and performance. Traditional relational databases scale vertically by upgrading hardware (e.g., adding CPU or memory to a single server), which becomes costly and limited. Distributed databases scale horizontally by adding more nodes, enabling them to handle larger workloads and data volumes. For instance, a social media app with millions of global users might use a distributed database to serve requests faster by storing data closer to users in regional nodes. However, distributed systems introduce complexity: queries may span multiple nodes, requiring coordination, and network latency can affect response times. While traditional databases excel at complex joins and transactions within a single node, distributed databases often optimize for read/write throughput by relaxing consistency (e.g., using eventual consistency models) or partitioning data to minimize cross-node operations.

Use cases and trade-offs further differentiate the two. Traditional relational databases are ideal for applications requiring strong consistency and complex transactions, such as banking systems or inventory management. They enforce strict schemas and relationships, which simplifies query logic but limits flexibility. Distributed databases suit scenarios demanding high availability, geographic redundancy, or massive scalability—like real-time analytics, IoT device tracking, or global e-commerce platforms. For example, a distributed database might replicate product inventory data across regions to prevent outages, even if temporary inconsistencies occur. However, distributed systems require careful design to handle partitioning strategies, node failures, and synchronization. Developers must choose based on priorities: consistency, scalability, or latency. While traditional databases offer simplicity and reliability for smaller-scale systems, distributed databases address modern needs for resilience and growth at the cost of increased operational complexity.