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hello in this video we will talk about an important topic of handling time related aspects in stream processing systems we will start by looking into why flink supports different notions of time and what exactly they are we will then describe the mechanism of watermarks that makes it possible to achieve reasonable trade-offs between the latency of the results and their correctness and in conclusion we will discuss what are late events and how you can handle them lets start with the discussion of the different notions of time as you probably know most messages in event driven systems have a timestamp field but what information does this field exactly contain one possibility which is the best in terms of correctness is event time event time is the time when an event actually occurred the timestamp in this case corresponds to the time recorded by the edge device producing the event in case we do not have a timestamp of the device or we cannot trust it the second best option is storage t