Pierre Lacave is a Senior Software Engineer at Corvil Ltd. He has contributed to more than 200 Corvil Analytics Plugins to decode, analyze, and enrich network data in enterprise and electronic trading infrastructure, and he’s also lead developer in the integration of the Corvil flagship product with the Hadoop/Spark ecosystem.
He studied computer science in Paris, France and Chongqing, China and holds a double MSC in software engineering.
High-speed machine-automated trading is now responsible for more than half of all US equity trades, and its continued growth across security markets seems assured. As individuals become less involved and machines drive these transactions to a greater extent, it is becoming increasingly important to closely monitor these automated transactions both at a high level and at a granular level to help inform future strategic decisions, control risk and ensure regulatory compliance. When it comes to processing and analyzing large-scale data for the trading of hundreds of millions of dollars, every bit matters. The insights derived from analysis in these environments are highly valuable to the financial audience. In this session, we will discuss using Spark to analyze trading activity in the world's largest financial institutions. We'll explore: - Why Spark is the right tool for analysis in these intense environments - Optimizing Spark applications to work with high volume real-time trading data - How to source authoritative, accurate monitoring data without impacting trading system performance