The project utilizes Big Data Analytics (BDA) to automate monitoring of synchrophasor recordings. This will improve assessing events that may affect power system resilience. The proposed BDA will be used to automatically extract knowledge leading to event analysis, classification and prediction, all used at different stages of the grid resilience assessment: operations, operations planning, and planning. The project’s techniques are based on their past work performed at the Texas A&M Engineering Experiment Station (TEES) on 5 automated classification of faults, location of faults and instability detection using neural network and machine learning classifiers and predictors, and the latest innovations in BDA techniques developed by Temple University. The team will engage Quanta Technology experts experienced in the utility interaction to interpret the PMU data files to be utilized in the process. They will facilitate industry feedback leading to the development of metrics for evaluation of the proposed solution. Additionally, the project proposes a novel solution for predicting future events based on historical PMU data by extracting the sets of precursors and analyzing the development of PMU observed disturbances over time.