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Akka Streams And Kafka Streams: Where Microservices Meet Fast Data

Why Microservices & Fast Data Belong Together...

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In a recent Lightbend survey, 75% of over 2400 developers reported to have at least some real-time functionality in their systems. Enterprises are realizing that the ability to extract value from streaming data in near real-time is the new competitive advantage.

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?

While both tools are part of Lightbend Fast Data Platform, they were designed for different needs and use cases: Akka Streams emerged as a dataflow-centric abstraction for the Akka Actor model, designed for general-purpose microservices, very low-latency event processing, and support for a wider class of application problems and third-party integrations via Alpakka. Kafka Streams, by comparison, is purpose-built for reading data from Kafka topics, processing it, and writing the results to new topics in a Kafka-centric way.

In this webinar by Dr. Dean Wampler, O’Reilly author and VP of Fast Data Engineering at Lightbend, we will:

  • Discuss the strengths and weaknesses of Akka Streams and Kafka Streams for particular design needs in data-centric microservices, including code examples from our Kafka Streams with Akka Streams tutorial.
  • Contrast them with Spark Streaming and Flink, which provide richer analytics over potentially huge data sets
  • Help you map these streaming engines to your specific use cases, so you confidently pick the right ones for your projects

Watch The Full Video + QA (56 Min)

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As always, Lightbend is here to make your streaming, Fast Data journey successful. To find out more about our platform subscription, getting-started engagement services, or anything else, feel free to contact us below and schedule a 20-min introduction.



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