In every organization the data is a significant part that can be separated as structured, unstructured and semi-structured. Unstructured data cannot be administered in the real-time by RDBMS or Hadoop. PySpark can be used for realtime data analysis of movie rating data collection. To generate the modified recommendations, method is intended that is Recommender Systems. Some complications that a Recommender System encountered are data sparsity, cold start, popular bias and scalability. In this research paper collaborative technique is applied for m-ovie rating recommender system. By getting benefits of collaborative filtering technique, we conclude that we use perspective metadata that is freely accessible. Concealed suggestions occur in the middle of movie ratings to personalize recommendations. Our recommended methodology demonstrate novelty by providing the modified recommendations irrespective of research field and of customer’s capability. By the usage of MovieLens dataset a significant development above supplementary standard techniques in evaluating global enactment and capability to yield appropriate and top-ratings at the top of the recommendation list.