Both streaming data and Complex Event Processing have an enormous impact on how companies can make strategic use of big data. With streaming data, companies are able to process and analyze this data in real time to gain an immediate insight. It often requires a two-step process to continue to analyze the key findings that might have gone unnoticed in the past.
With CEP approaches, companies can stream data and then leverage a business process engine to apply business rules to the results of that streaming data analysis. The opportunities to gain insights that lead to new innovation and new action are the foundational value of streaming data approaches.
So, what is the difference between CEP and streaming data solutions? While stream computing is typically applied to analyzing vast amounts of data in real time, CEP is much more focused on solving a specific use case based on events and actions.
However, a streaming data technique is often used as an integral part of a CEP application. Streaming data applications typically manage a lot of data and process it at a high rate of speed. Because of the amount of data, it is typically managed in a highly distributed clustered environment.
CEP, on the other hand, typically will not manage as much data, so it is often run on less complex hardware. In addition, the type of analysis will be different. It is critical that CEP applications be able to connect to key systems of record such as customer relationship management (CRM) systems or transaction management environments.
It is not uncommon for CEP environments to deal with only a few variables that are applied to very complex models and processes. While relying on complex mining or statistical models, CEP systems are designed around a rules engine so that when an event takes place, the rules engine triggers an action.