anjijava16 · May 30, 2021 12:44
diff --git a/streaming_processing.scala b/streaming_processing.scala
 Understand the unique processing characteristics of stream processing: 

 This includes the difference between event time and processing time, sliding and tumbling windows, latearriving data and watermarks, 
 and missing data. 

 i.   Event time is the time that something occurred at the place where the data is generated. 
 ii.  Processing time is the time that data arrives at the endpoint where data is ingested. 
 iii. Sliding windows are used when you want to show how an aggregate, such as the average of the last three values, change over time, 
 and you want to update that stream of averages each time a new value arrives in the stream. 
 iv. Tumbling windows are used when you want to aggregate data over a fixed period of time for example, for the last one minute.
	Understand the unique processing characteristics of stream processing:

	This includes the difference between event time and processing time, sliding and tumbling windows, latearriving data and watermarks,
	and missing data.

	i. Event time is the time that something occurred at the place where the data is generated.
	ii. Processing time is the time that data arrives at the endpoint where data is ingested.
	iii. Sliding windows are used when you want to show how an aggregate, such as the average of the last three values, change over time,
	and you want to update that stream of averages each time a new value arrives in the stream.
	iv. Tumbling windows are used when you want to aggregate data over a fixed period of time for example, for the last one minute.