If you’re building streaming data apps, your first inclination might be to reach for Spark Streaming, Flink, Apex, or similar tools, which run as services to which you submit jobs for execution. But sometimes, writing conventional microservices with embedded stream processing is a better fit for your needs.
Kafka Streams is purpose-built for reading data from Kafka topics, processing it, and writing the results to new topics. With powerful stream and table abstractions and an exactly once capability, it supports a variety of common scenarios involving transformation, filtering, and aggregation. Akka Streams emerged as a dataflow-centric abstraction for the Akka Actors model, designed for general-purpose microservices, especially when per-event low-latency is important. Most systems provide efficient processing amortized over sets of records, but usually not at end-to-end low latency per event (e.g., for complex event processing in true real-time applications). Also because of its general-purpose nature, Akka Streams supports a wider class of application problems and third-party integrations but is less focused on Kafka-based applications. Both are primarily libraries that you integrate into your microservices, which means you must manage their lifecycles yourself, but you also get lots of flexibility to do this as you see fit.
In contrast, Spark Streaming and Flink run their own services. You write “jobs” or use interactive shells that tell these services what computations to do over data sources and where to send results. Spark and Flink then determine what processes to run in your cluster to implement the dataflows. Hence, there is less of a DevOps burden to bear but also less flexibility when you might need it. Both systems are also more focused on data analytics problems, with various levels of support for SQL over streams, machine learning model training and scoring, etc.
Join Dean Wampler and Boris Lublinsky to learn how to build two microservice streaming applications based on Kafka using Akka Streams and Kafka Streams for data processing. You’ll explore the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you’ll know when to choose them instead. You’ll be given an execution environment and the code examples in a GitHub repo, and Dean and Boris will walk you through the examples, interspersed with short presentations, helping you understand their strengths, weaknesses, performance characteristics, and lifecycle management requirements.
Dean Wampler is the vice president of fast data engineering at Lightbend, where he leads the Lightbend Fast Data Platform project, a distribution of scalable, distributed stream processing tools including Spark, Flink, Kafka, and Akka, with machine learning and management tools. Dean is the author of Programming Scala and Functional Programming for Java Developers and the coauthor of Programming Hive, all from O’Reilly. He’s a contributor to several open source projects. A frequent Strata speaker, he’s also the co-organizer of several conferences around the world and several user groups in Chicago.
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. Over his career, he’s been responsible for setting architectural direction, conducting architecture assessments, and creating and executing architectural road maps in fields such as big data (Hadoop-based) solutions, service-oriented architecture (SOA), business process management (BPM), and enterprise application integration (EAI). Boris is the coauthor of Applied SOA: Service-Oriented Architecture and Design Strategies, Professional Hadoop Solutions, and Serving Machine Learning Models. He’s also cofounder of and frequent speaker at several Chicago user groups.
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