To achieve the lowest possible recovery point objectives (RPOs) and recovery time objectives (RTOs) for crucial AI applications, near-synchronous replication provides the best of both worlds: the great performance of synchronous replication without the significant network or infrastructure demands it requires. Near-synchronous replication is comparable to synchronous replication – although it is technically asynchronous because data is written to several locations simultaneously, except for a brief lag between the primary and secondary locations. Near-synchronous replication is always on and constantly replicates only changed data to poland whatsapp number data the recovery site within seconds. Because it is always on, it doesn’t require scheduling or using snapshots. It writes to the source storage without having to wait for the target storage to acknowledge it. One of the key benefits of near-synchronous replication is that it offers strong data availability and security at quicker write speeds than synchronous replication. Because of this, it’s a solid option for workloads like AI applications with a lot of data or heavy write loads.
AI Data Mobility Can Be a Major Problem for IT Infrastructure
AI is data-driven. The amount of AI data in existence is exponentially greater than anything IT has previously encountered, and the scope represents a totally new age of data generation. Exabytes of raw data are needed for even basic AI applications, which must be prepared for model training and subsequent inference. The data sets are frequently created on the edge and must be moved into a central data repository for processing. Additionally, the data needs to be stored for possible re-training at the end of its lifecycle. The need for continuous movement of enormous volumes of data has created new issues for IT infrastructure and management: Today’s network technologies and synchronous replication-based data management solutions aren’t equipped to lift and move these massive data sets. To move AI data with limited processing power and bandwidth, asynchronous replication is required. This guarantees block-level, continuous replication at low bandwidth, preventing significant data transfer peaks.