Spark Streaming processes a continuous stream of data by dividing the stream into micro-batches called a Discretized Stream or DStream. DStream is an API provided by Spark Streaming that creates and processes micro-batches. DStream is nothing but a sequence of RDDs processed on Spark's core execution engine like any other RDD. DStream can be created from any streaming source such as Flume or Kafka.
As shown in the following Figure 5.1, input data from streaming sources are received by the Spark Streaming application to create sub-second DStreams, which are then processed by the Spark core engine. Batches of each output are then sent to various output sinks. The input data is received by receivers and distributed across the cluster to form the micro-batch. Once the time interval completes, the micro-batch is processed through parallel operations such as join, transform, window operations, or output operations.
From deployment and execution perspective, Spark...