Traditional business rule applications process records of a few megabytes of data at a time, one record at a time. As we move solutions to big data, rules may be applied to terabytes of data, and traditional IBM Operational Decision Manager (ODM) architecture needs to keep up with the increased demand. To scale business rules solutions up to the world of big data, this session presents an integration of ODM with Apache Spark and Hadoop MapReduce.Speakers: Pierre Feillet, Product architect, IBM Operation Decision ManagerPierre is software architect at IBM, working on large scale decision automation. He has worked in symbolic artificial intelligence over the past 15 years at IBM and ILOG. He has been involved in business rule systems as runtime architect to shape product offerings named IBM Operational Decision Manager and previously ILOG JRules. In the software industry since 1992 he has gathered significant experience and expertise in engine integration, enterprise architecture and cloud.He is currently responsible for driving the IBM Business Rule platform evolution to expand rule engines in the cloud. Pierre also promotes AI combinations mixing rules with big data, analytics and machine learning in Spark and Hadoop.Pierre directs a thesis about decision explanation through a causal model. He is member for IBM of the Association Française d’Intelligence Artificielle.Nigel Crowther, Smarter Process Technical LeadNigel works in the IBM Cloud Client Technical Engagement specializing in Smarter Process (ODM, DSI). He has over 10 years’ experience in designing and developing Smarter Process systems and 25 years’ experience in IT. Nigel has experience across all business sectors with his greatest focus on finance. Nigel is a published author on DeveloperWorks and IBM Redbooks. He has presented at conferences on topics on rule governance, big data and hot entities.