The Hadoop Distributed File System (HDFS) is a distributed file system that runs on standard or low-end hardware. Developed by Apache Hadoop, HDFS works like a standard distributed file system but provides better data throughput and access through the MapReduce algorithm, high fault tolerance and native support of large data sets.
A recovery point objective (RPO) is the maximum acceptable amount of data loss measured in time. It is the age of the files or data in backup storage required to resume normal operations if a computer system or network failure occurs.
RPO is measured in time and then dictates disaster recovery procedures. For example, if the RPO is set to 30 minutes, then a backup of the system is required to be done every 30 mintues. The RPO must be completely independent of the minimum estimated time required to resume normal operations after a disaster, known as the recovery time objective (RTO). Along with the RTO, the RPO helps system administrators determine appropriate disaster recovery policies and procedures and decide which backup and recovery technologies to employ given the overall design strategy that data loss should not delay the expected time to resume normal operations. For example, a two-hour RPO might be appropriate for auxiliary hard drives, while a five-day RPO might be appropriate for magnetic tape or recordable compact disks.
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