Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in consumption of processing resources such as CPU consumption. In this paper, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider a deadline as our constraint and before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. We have used a set of data sets and applications in the evaluation phase. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.