Spark Streaming is built on an abstraction called Discretized Streams referred, to as DStreams. A DStream is represented as a sequence of RDDs, with each RDD created at each time interval. The DStream can be processed in a similar fashion to regular RDDs using similar concepts such as a directed cyclic graph-based execution plan (Directed Acyclic Graph). Just like a regular RDD processing, the transformations and actions that are part of the execution plan are handled for the DStreams.
DStream essentially divides a never ending stream of data into smaller chunks known as micro-batches based on a time interval, materializing each individual micro-batch as a RDD which can then processed as a regular RDD. Each such micro-batch is processed independently and no state is maintained between micro-batches thus making the processing stateless by nature. Let's...
