Most cloud data centers are over-provisioned and underutilized, primarily to handle peak loads and sudden failures. This has motivated many researchers to reclaim the unused resources, which are by nature ephemeral, to run data-intensive applications at a lower cost. Hadoop MapReduce is one of those applications. However, it was designed on the assumption that resources are available as long as users pay for the service. In order to make it possible for Hadoop to run on unused (ephemeral) resources, we have designed a heterogeneity and volatility-aware holistic scheduler consisting of three different components: (1) A MapReduce task and job scheduler that relies on a global vision of resource utilization predictions, (2) a scheduler-based data placement strategy that improves the data locality, and (3) a reactive QoS controller that ensures customers’ service-level agreement (SLA) and minimizes interference between co-located workloads. Our framework makes it possible to take advantage of ephemeral resources efficiently. Indeed, for a given set of jobs, it reduces the overall execution time by up to 47.6% and an average of 18.7% as compared to state-of-the-art strategies.