Over the course of this chapter, the concepts of the stream-processing system, Spark Streaming, DStreams in Apache Spark, DStreams, DAG and DStream lineages, and transformations and actions were covered. Additionally, window-stream processing and a practical example of processing Twitter tweets using Spark Streaming were covered. Then, the receiver-based and direct-stream approaches of data consumption were covered with regards to Kafka, and finally, the newly developing technology of Structured Streaming was covered. Currently, it aims to solve many current challenges, such as fault tolerance, the use of exactly-once semantics in the stream, and the simplification of the integration with messaging systems, such as Kafka, while maintaining flexibility and extensibility to integrate with other input stream types.
In the next chapter, we will explore Apache Flink, which is a key challenger to Spark as a computing platform.
