Implementing Schema Evolution in Real-Time Analytics Architectures
Santosh Vinnakota · International Scientific Journal of Engineering and Management · 2024
Abstract—The increasing demand for real-time analytics necessitates robust schema evolution mechanisms to accommodate dynamic changes in data structures without disrupting ongoing operations. This paper explores schema evolution strategies in real-time analytics architectures, highlighting best practices, challenges, and implementation methodologies. We discuss techniques such as schema-on- read, schema registry, and schema migration, supported by modern data streaming frameworks like Apache Kafka, Apache Flink, and Apache Iceberg. Additionally, we provide an implementation framework with practical considerations for ensuring consistency, compatibility, and minimal latency. Keywords—Schema Evolution, Real-Time Analytics, Apache Kafka, Apache Flink, Apache Iceberg, Schema Registry, Data Streaming, Schema Migration, Data Lake, Schema Drift