Just like all the other components of Spark, Spark Streaming is also scalable and fault-tolerant, it's just that it manages a stream of data instead of a large amount of data that Spark generally does. The way that Spark Streaming approaches streaming is unique in the sense that it accumulates streams into small batches called DStreams and then processes them as mini-batches, an approach usually called micro-batching. The component that receives the stream of data and splits it into time-bound windows of batches is called the receiver.
Once these batches are received, Spark takes these batches up, converts them into RDDs, and processes the RDDs in the same way as static datasets. The regular framework components such as the driver and executor stay the same. However, in terms of Spark Streaming, a DStream or Discretized stream is just a continuous stream of RDDs. Also, just like SQLContext served as an entry point to use SQL in Spark...