Designs and implements high-performance event-driven stream processing topologies using Java and Kotlin.
This skill provides expert-level guidance for building complex Kafka Streams applications, helping developers navigate the nuances of KStream, KTable, and GlobalKTable abstractions. It excels at designing robust stream processing workflows, implementing stateful operations like windowed joins and aggregations, and ensuring data integrity through exactly-once semantics (EOS). Whether you are building real-time analytics dashboards, data enrichment pipelines, or event-driven microservices, this skill optimizes your topology for scalability, fault tolerance, and efficient state management.
주요 기능
0113 GitHub stars
02Configuration of advanced windowing strategies like Tumbling, Hopping, and Session windows
03Topology optimization through state store management and repartitioning reduction
04Expert differentiation between KStream, KTable, and GlobalKTable patterns
05Data consistency guidance with Exactly-Once Semantics (EOS v2) setup
06Implementation of complex join strategies including Stream-Stream and Stream-Table joins
사용 사례
01Implementing event-driven microservices that require complex temporal joins
02Enriching real-time event streams with reference data from external look-up tables
03Building stateful aggregations for real-time monitoring and anomaly detection
