Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.