Cloud infrastructure services such as Amazon EMR allow users to have access to tailor-made Big Data processing clusters within a few clicks from their web browser, thanks to the elastic property of the cloud. In virtual cloud environments, resource management is desired to be performed in a way which optimizes utilization, thus maximizing the value of the resources acquired. As cloud infrastructures become increasingly popular for Big Data analysis, the execution of programs with respect to user selected performance goals, such as job completion deadlines, remains a challenge. In this work we present BARBECUE (joB AwaRe Big-data Elasticity CloUd managEment System), a system that allows a Hadoop MapReduce virtual cluster to automatically adjust its size to the workload it is required to execute in order to respect individual jobs’ completion deadlines without acquiring more resources than the least necessary. To that end, BBQ’s Decision Making module uses a Performance Model for MapReduce jobs which can express cluster resources (i.e., YARN Container capacity) and execution time as a function of the number of nodes in the cluster. BBQ leverages the abstraction of YARN, making it feasible for integration with other execution frameworks, such as Spark, with the necessary changes to its pluggable Decision Making module. We also add a new feature to Hadoop MapReduce which can now dynamically, on-the-fly update the number of selected ReduceTasks in cases where the cluster is expanded, so that our system makes full use of the resources it has acquired during the reduce phase of the execution. BBQ uses an adaptation of the hill climbing algorithm to estimate the optimal combination of number of nodes and reduce waves given a known job, its data input and an execution deadline. The attendees will be able to watch the system perform cluster resizes in real-time in order to execute its assigned jobs in time.