In recent years, the internet has become faster, computer storage has become larger, data collected by organizations as well as web and social media has grown tremendously. Data Mining tools require adaptations to cope with analyzing massive amounts of data. Ecosystems like Hadoop, MapReduce, Spark, and other cloud platforms have emerged to store, manage, and process large data in reasonable time. Recent work has adapted certain machine learning tools to run on these systems. However, many rule extraction data mining tools still lack adaptation or software packages for processing on cloud platforms, which presents a challenging problem. Actionable Pattern Mining is a type of rule extraction data mining approach for discovering actionable knowledge, that the user can utilize to their advantage. Traditional classification rules predict a class label of a data object. In contrast, Action Rules produce actionable knowledge or suggestions on how an object can change from one class value to another more desirable one. In this work, we discuss association and classification rule mining algorithms with distributed environments. We focus on Actionable Pattern Discovery and propose several approaches to adapt this method for processing in a distributed environment, and extract actionable patterns from big data using distributed computing frameworks. We experiment with several datasets, including Car Manufacturing, Mammographic Mass, Charlotte Business Wise data, Net Promoter Score business data, and Hospital Readmission data. Results show that rule mining algorithms can successfully adapt to cloud computing environment, in order to scale and handle big data. We show that such an adaptation improves the execution time efficiency of rule mining algorithms. Application domains which can benefit from this work include: Medical, Financial, Manufacturing, Education, and Social Networks.