I'll start with the premise that Kafka is the ideal backplane for reliable capture and organization of data streams for downstream consumption. Then, I'll present microservice applications implemented using Akka Streams and Kafka Streams on top of Kafka. The goal is to understand the relative strengths and weaknesses of these toolkits for building Kafka-based streaming applications. I'll also compare and contrast them to Spark Streaming and Flink, to understand when those tools are better choices. Briefly, Akka Streams and Kafka Streams are best for data-centric microservices, while Spark Streaming and Flink are best for richer analytics over large volume streams where scalability through "automatic" partitioning is required.