This chapter focused on handling streaming data from sources such as Kafka, socket, and filesystem. We also covered various stateful and stateless transformation of DStream along with checkpointing of data. But chekpointing of data alone does not guarantee fault tolerance and hence we discussed other approaches to make Spark Streaming job fault tolerant. We also talked about the transform operation, which comes in handy where operations of RDD API is not available in DStreams. Spark 2.0 introduced structured streaming as a separate module, however, because of its similarity with Spark Streaming, we discussed the newly introduced APIs of structured streaming also.
In the next chapter, we will focus on introducing the concepts of machine learning and then move towards its implementation using Apache Spark MLlib libraries. We will also discuss some real-world problems using Spark MLlib.