A streaming mapping receives data from unbounded data sources. An unbounded data source is one where data is continuously flowing in and there is no definite boundary. Sources stream data as events. The Spark engine receives the input data streams and divides the data into micro batches. The Spark engine processes the data and publishes data in batches.
The following image shows how the Spark engine receives data and publishes data in batches:
The Spark engine uses Spark Streaming to process data that it receives in batches. Spark Streaming receives data from streaming sources such as Kafka and divides the data into discretized streams or DStreams. DStreams are a series of continuous streams of Resilient Distributed Datasets (RDD).