With emerging Network Functions Virtualization (NFV) and Software Defined Networking (SDN) paradigms in Network Management (NM), new network devices and features can immediately become available. Available network resources and services can be altered and optimized in real time to gain the maximum benefit. However, this requires real time analytics information sent to SDN controllers rather than traditional manual offline or batch analytics which deliver outputs in hourly or monthly in NM Systems. As a result, real time analytic is becoming a critical element for NM. In this paper, we describe a configuration free (i.e., non-parametric) streaming data anomaly detection analytic engine for automatic NM system development. We describe the design principles, innovative algorithm design, architecture and implementation of the engine in relation to streaming data and mobile NM. Finally, we present use cases and evaluation results.