CEP is primarily used for analysis/analytics on discrete business events. Events are correlated in time using simple IF/THEN/ELSE logic. The events need not be of a single type or category. The data encapsulated in the business events are primarily structured in their form. The common CEP engines support modest data rates or around 10K messages/second with a latency typically in the 'seconds' range. The maximum data processing rates can scale up to around 100K events/second.
Streams on the other hand is designed to handle processing rates which are an order of magnitude higher than CEP. It can handle around millions of events per second with built-in linear scalability. Streams data sources are typically of a single event type e.g. camera feeds from traffic signals, sensor data generated from a pipeline or medical device, and so on. Streams is designed to handle the full gamut of unstructured data and contrary to IF/THEN/ELSE based logic in CEP, it is capable of performing advanced analytics on the data set. Examples of advanced analytics are only limited by the power of the mathematical and statistical models. Fast Fourier Transforms, Holt Winters algorithm, time series analysis algorithms would be some real world examples.
To summarize, although both Streams and CEP fall under the category of 'Continuous Intelligence', keep the following image in mind when any of your colleagues engage in the discussion: