The term "data replication" refers to the continuous or periodic duplication of data between two or more storage locations, systems, or databases. The objective of data replication is to enhance data availability, fault tolerance, load balancing, and performance in distributed environments. It is commonly used in scenarios with high requirements for availability and data security, such as in cloud infrastructures, databases, or hybrid IT architectures.
Real-time replication: Immediate synchronization of data changes between source and target systems.
Asynchronous replication: Delayed data transfer to decouple source and target systems.
Replication policies: Configurable rules for replication frequency, data filtering, and target systems.
Conflict detection and resolution: Mechanisms to identify and resolve conflicting changes to replicated data.
Monitoring and logging: Detailed logs, monitoring tools, and status indicators to oversee replication activities.
Scalability and load balancing: Support for multiple replication targets to improve availability and performance.
Security features: Encryption of transferred data, as well as authentication and access control.
Snapshot-based replication: Replication of data states at defined points in time (e.g., for backup purposes).
A company replicates its central customer database to a data center at a different location to increase fault tolerance.
An e-commerce provider uses replication to synchronize product information in real time between the main system and several regional web servers.
An international organization replicates ERP data between offices in Europe and Asia to accelerate local data access.
A backup system creates hourly snapshots and transfers them automatically to the cloud.
A database solution detects conflicts in parallel data changes and provides rules for resolution.