The concept of continuous applications

Streaming apps tend to grow in complexity. Streaming computations don't run in isolation; they interact with storage systems, batch applications, and machine learning libraries. Therefore, the notion of continuous applications--in contrast to batch processing--emerged, and basically means the composite of batch processing and real-time stream processing with a clear focus of the streaming part being the main driver of the application, and just accessing the data created or processed by batch processes for further augmentation. Continuous applications never stop and continuously produce data as new data arrives.

True unification - same code, same engine

So a continuous application could also be implemented on top of RDDs and DStreams but would require the use of use two different APIs. In Apache Spark Structured Streaming the APIs are unified. This unification is achieved by seeing a structured stream as a relational table without boundaries where new...