Hi Everyone, Im developing a new Flink Application (real time stock out) which will be reading from various Kafka topics ( 6 so far ) . I had some questions I needed to clarify from the experts here: 1. All topics are of different schema's , So I started with creating individual source in flink per topic. I believe this is correct ? 2. I wanted to use event time as this is important for this application ( Important wrt order of the events) . But this is not needed for all the topics I consume from as some of them are low throughput reference data. When I say event time, I dont want to consider late arriving data. I added watermark in Kafka Source itself as i read about per partition watermarks. I wanted to clarify if this is necessary or simply reading with Process time semantics and in the Joins ( KeyedCoprocessFunctions etc ) just keeping the last read business event timestamp and only considering newer one's will do ? a. One reason for this thought is because the topic contains data from all markets and I dont want by introducing event time and lateness configs to ignore a different market's event as late arriving. Another is as mentioned above, the latest event for a key is whats important for me. b. The thing that's worrying about event time and watermarks is that since Im reading from multiple topics with different throughputs I dont want a situation where data is not emitted due to watermark issues as this application needs minimum end to end latency.