Using several example microservice applications, I'll compare and contrast using Akka Streams and Kafka Streams for stream processing with Kafka as the data "backplane". I'll discuss the strengths and weaknesses of each tool for particular design needs, so you'll feel better informed when making choices. I'll also contrast them with Spark Streaming and Flink, including when to chose them instead. For example, Akka and Kafka Streams are applications you embed in your microservices, while Spark and Flink are services to which you submit jobs to perform. Required knowledge Some prior experience with Kafka is useful. Experience with Akka Streams and Kafka Streams is not required. Learning objectives In this talk, you'll learn the following: * How to combine Kafka with Akka Streams and Kafka Streams to implement stream processing microservices. * How to leverage the strengths of these tools while avoiding their weaknesses. * How these libraries compare to Spark Streaming and Flink for stream processing. Boris Lublinsky is a software architect at Lightbend, where he specializes in big data, stream processing, and services. Boris has over 30 years experience in enterprise architecture and has been accountable for setting architectural direction, conducting architecture assessments, and creating and executing architectural roadmaps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration