  Schema evolution is critical in managing database systems to ensure
compatibility across different data versions. A schema registry typically
addresses the challenges of schema evolution in real-time data streaming by
managing, validating, and ensuring schema compatibility. However, current
schema registries struggle with complex syntactic alterations like field
renaming or type changes, which often require significant manual intervention
and can disrupt service. To enhance the flexibility of schema evolution, we
propose the use of generalized schema evolution (GSE) facilitated by a compound
AI system. This system employs Large Language Models (LLMs) to interpret the
semantics of schema changes, supporting a broader range of syntactic
modifications without interrupting data streams. Our approach includes
developing a task-specific language, Schema Transformation Language (STL), to
generate schema mappings as an intermediate representation (IR), simplifying
the integration of schema changes across different data processing platforms.
Initial results indicate that this approach can improve schema mapping accuracy
and efficiency, demonstrating the potential of GSE in practical applications.
