The paper describes an application of Mitek's Big Data Analytics software called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM detects abnormaly performing aircraft, abnormal flight-to-flight trends, and individual flight anomalies by fitting a large scale multi-level regression model to the data set. Using DFM, a multi-terabyte airline data set with a half million flights was processed in a few hours. The anomalies found include wrong values of computed variables such as aircraft weight and angle of attack as well as failures, biases, and trends in flight sensors and actuators. These anomalies were missed by the existing FOQA data exceedance monitoring currently used by the airline.