AI BriefingKakao Reveals Spark Job Design for Consistency Checks Across ~300 CDC Pipelines
Key point
Kakao has revealed a Spark job design featuring four-stage modularization and design patterns, including Reader and Translator, to ensure data reliability across approximately 300 CDC pipelines.
Details
Kakao developed a Scala-based Spark job to verify data consistency across approximately 300 CDC pipelines. This system submits JSON on a per-table basis to minimize the overall impact of job failures and consists of four modules: Reader, Translator, Comparator, and Reporter. Each component ensures extensibility through abstract class and subclass inheritance, implementing a flexible structure by applying various design patterns such as Factory, Template Method, Strategy, and Singleton. Additionally, code style consistency is maintained using automation tools like scalafmt and scalafix.
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