Engine module performance trending and engine system anomaly detection and identification are core capabilities for any engine Condition Based Maintenance system. The genesis of on-condition monitoring can be traced back nearly 4 decades, and a methodology known as Gas Path Analysis (GPA) has played a pivotal role in its evolution. GPA is a general method that assesses and quantifies changes in the underlying performance of the major modules of the engine (compressors and turbines) which directly affect performance changes of interest such as fuel consumption, power availability, compressor surge margins, and the like. This approach has the added benefit in that it enables anomaly detection and identification of many engine system accessory faults (e.g., variable stator vanes, handling and customer bleeds, sensor biases and drift). Legacy GPA has been confined to off-board analysis of snapshot data averaged over a stable flight conditions when the engine is in steady state operation. This discrete data point approach, while fairly accurate and repeatable, comes with a price tag which is a time latency to detect (and subsequently isolate) a faulty condition. In this paper, we explore the use of streaming full flight data which includes both transient and steady state operation. This type of data stream, when properly processed, allows faster anomaly detection, credible fault persistency checks and timely fault identification. This paper outlines the use of companion engine data to achieve a cross-wing comparative diagnostic method.