Nowadays analyzing vast amount of sensor data in real-time for process optimization and event prediction has become a major task for a production line. Major questions focus on how to build a system to process multiple gigabyte data per second on various locations. In this presentation we focus at a BigData analytics platform which is built on top of the SMACK stack, it is an acronym for Spark, Mesos, Akka, Cassandra and Kafka. SMACK provides an ecosystem for handling high frequent low-level events and for the creation of high-level events to trigger business processes as a reaction to derived knowledge in a bimodal IoT architecture. To go into detail, we break down to a stream based approach, SMACK components and interaction between them. The central parts of the system (Spark Streams, Akka Streams, Akka Actors) will be revealed on concrete code snippets in Scala. Learning outcomes for attendees: better understanding of SMACK, benefits of a pure functional approach for building scalable platforms, experiences and best practices for working with SMACK, assets of bimodal architecture for combining IoT and BPM.
Yevgen Pikus studied computer since at TU Braunschweig. During the study he was an active member of Scala User Group Braunschweig. Since December 2015 he is a researcher and a Ph.D. student at Fraunhofer ISST in Dortmund and he is working on the development of IoT ecosystems, BigData platforms and business processes management applications.
