What is the difference between strong consistency, eventual consistency, and causal consistency?

Consistency models define how and when updates to distributed data become visible across nodes.

Strong consistency ensures all reads return the latest write, but it often requires synchronization and higher latency. Eventual consistency allows replicas to diverge temporarily but guarantees convergence over time, improving availability and performance. Causal consistency sits between them, ensuring that causally related operations are seen in order while unrelated ones may be seen in different orders. These models are fundamental when designing distributed data systems, especially in choosing between latency, availability, and correctness.