  BGP (Border Gateway Protocol) serves as the primary routing protocol for the
Internet, enabling Autonomous Systems (individual network operators) to
exchange network reachability information. Alongside significant on-going
research and development efforts, there is a practical need to understand the
nature of events that occur on the Internet.
  Network operators are acutely aware of security-related incidents such as
'Prefix Hijacking' as well as the impact of network instabilities that ripple
through the Internet. Recent research focused on the study of BGP anomalies
(both network/prefix instability and security-related incidents) has been based
on the analysis of historical logs. Further analysis to understand the nature
of these anomalous events is not always sufficient to be able to differentiate
malicious activities, such as prefix- or sub-prefix- hijacking, from those
events caused by inadvertent misconfigurations. In addition, such techniques
are challenged by a lack of sufficient resources to store and process data
feeds in real-time from multiple BGP Vantage Points (VPs).
  In this paper, we present a BGP Deep-analysis application developed using the
PNDA (Platform for Network Data Analytics) 'Big-Data' platform. PNDA provides a
highly scalable environment that enables the ingestion and processing of 'live'
BGP feeds from many vantage points in a schema-agnostic manner. The Apache
Spark-based application, in conjunction with PNDA's distributed processing
capabilities, is able to perform high-level insights as well as
near-to-real-time statistical analysis
