We are going to discuss the generation of risk metrics for trades by supplying market data on a timed basis using a streaming Dataflow Engine running Apache Beam.
Fred Tsang (Fred) introduces the basics concepts around streaming systems. He starts with some basic ideas centred around Apache Beam with particular reference to streaming. He then further expands on the execution of an Apache Beam streaming pipeline in Google Dataflow
Fred Tsang (Fred) introduces the basics concepts around streaming systems. He starts with some basic ideas centred around Apache Beam with particular reference to streaming. He then further expands on the execution of an Apache Beam streaming pipeline in Google Dataflow
Sanjeev Nutan (Sanj) discusses the mechanism for delta risk generation on Interest Rates Swaps using Beam and Dataflow. He introduces the concepts around affinity and state (or buckets)
Mariusz Izydorek (Mariusz) explains how he has integrated Mircosoft Excel with Google PubSub to dynamically display the Delta risk.
Mariusz Izydorek (Mariusz) explains how he has integrated Mircosoft Excel with Google PubSub to dynamically display the Delta risk.
Mariusz then goes on to describe the technical architecture that allowed him to integrate Excel with Google PubSub
Finally, Raj Subramani (Raj) discusses the results of running various scenarios through this streaming pipeline. Raj explains how scalability was achieved with Google Dataflow to meet some exacting performance requirements.
Finally, Raj Subramani (Raj) discusses the results of running various scenarios through this streaming pipeline. Raj explains how scalability was achieved with Google Dataflow to meet some exacting performance requirements.