Solution patterns for realtime streaming analytics

Large-scale data analytics has received much attention under the theme "Big Data". Big data usecases have found a wide range of applications from individual health monitoring to urban planning. Even at this initial stage, big data has demonstrated it's potential to transform the world. Although most early use cases used batch processing technologies like MapReduce, there are many usecases such as stock markets, traffic, surveillance, and patient monitoring that need realtime analytics. Realtime Analytics Technologies like Apache Storm, Spark Streaming, and several Complex Event Processing systems have received attention under realtime analytics. However, most practitioners still focus on implementing realtime analytics from the scratch. There is no common shared understanding about how to implement those analytics usecases among the early adopters. This tutorial tries to address this gap by describing thirteen common relatime analytics patterns and explaining how to implement them. In the discussion, we will draw heavily from real life usecases done under Complex Event Processing and other technologies.