Data replication, the main failure resilience strategy used for big data analytics jobs, can be unnecessarily inefficient. It can cause serious performance degradation when applied to intermediate job outputs in multi-job computations. For instance, for I/O-intensive big data jobs, data replication is especially expensive because very large datasets need to be replicated. Reducing the number of replicas is not a satisfactory solution as it only aggravates a fundamental limitation of data replication: its failure resilience guarantees are limited by the number of available replicas. When all replicas of some piece of intermediate job output are lost, cascading job recomputations may be required for recovery.