In this post, author Boris Lublinsky demonstrates with code how dynamically controlled streams can be implemented leveraging Kafka Streams, and the main properties of such implementation.

This post discusses stateful processing considerations when deciding whether your use case should be stateless or stateful when implementing stream processing.

This is the second of six posts in our cloud-native series that focuses on streaming data applications, exploring an alternative class of compute tools–libraries that provide streaming semantics–but which you embed in your microservices.

Two technologies–Akka Streams and Kafka Streams–have emerged as popular tools to use with Apache Kafka for addressing the shared requirements of availability, scalability, and resilience for both streaming microservices and Fast Data. So which one should you use for specific use cases?

In this talk by Dean Wampler, PhD., VP of Fast Data Engineering at Lightbend, we’ll look at the criteria you need to consider when selecting technologies, plus the context and background to make good decisions when it comes to adopting streaming frameworks.

In this webinar, Dr. Dean Wampler, VP of Fast Data Engineering at Lightbend, discusses the strengths and weaknesses of Kafka Streams and Akka Streams for particular design needs in data-centric microservices.