Splitschemas

Splitschemas refers to the practice of partitioning a data model into multiple smaller schemas, each encapsulating a bounded context or domain area. It aims to improve modularity, scalability, and independent evolution of data definitions.

In relational databases, split schemas can mean using separate database schemas (namespaces) within a single DB instance or separate databases; cross-schema references are possible but require careful handling of permissions and integrity constraints. In document stores or data graphs, split schemas refer to modular collections or graphs that reference each other.

Key concepts include a base or common schema with shared types, per-domain schemas containing domain entities, and an orchestration layer to query across schemas, either via joins, federation, or API composition. They often rely on schema registries or versioning to track changes.

Benefits of split schemas include better separation of concerns, targeted access control, and parallel development, as well as easier migration and rollback for a domain. Challenges include cross-schema queries that can be complex, potential performance overhead, maintaining consistency, and deployment complexity.

Common use cases encompass multi-tenant systems with per-tenant schemas, large products with distinct bounded contexts, analytics pipelines that separate facts and dimensions, and federation of microservices with shared data models. See also related topics such as schema migration, bounded context, schema federation, and data governance.