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Mechanisms and patterns for copying and synchronizing data across storage systems to improve availability, scalability, and fault tolerance.
Data replication refers to strategies and techniques for distributing and synchronizing copies of data across nodes, sites, or systems. It addresses availability, latency, consistency and scalability in distributed architectures. Choosing synchronous, asynchronous or log‑based replication determines fault tolerance, performance and operational effort.
Delay between write on primary and visibility on replica.
Percentage of completed replication operations without inconsistencies or errors.
Amount of data per second reliably transferred to replicas.
Use of synchronous or asynchronous streaming replication to create standby servers for high availability and read scaling.
Distributed replication model with consensus‑based group coordination for automated failure handling and conflict resolution.
Log‑based CDC pipelines that extract changes from transaction logs and replicate them to targets like Kafka or data warehouses.
Analyze requirements: determine consistency, latency, and fault tolerance needs.
Choose replication strategy (synchronous, asynchronous, log‑based) and target architecture.
Provision replication endpoints, monitoring and failover procedures; test under load and failure scenarios.