Smart Grid is the solution to solve existing problems of energy security from generation to utilization. Smart grid is a premium source of Big Data. The data should be processed to reveal hidden patterns and secret correlations in order to extrapolate the needed values. Such useful information obtained by the so-called data analytics is an essential element for energy management and control decision toward improving energy security, efficiency, and decreasing costs of energy use. For that reason, different techniques have been developed to process Big Data. This paper presents an overview of these techniques and discusses their advantages and challenges. The contribution of this paper is building a recommender system using different techniques to overcome the most obstacles encountering the Big Data processes in smart grid. The proposed system achieves the goals of the future smart grid by (i) analyzing data and executing values as accurately as possible, (ii) helping in decision making to improve the efficiency of the grid, (iii) reducing cost and time, (iv) managing operating parameters, (v) allow predicting and preventing equipment failures, and (vi) increasing customer satisfaction. Big Data process enables benefits that were never achieved for the smart grid application.