Compatibility and data models
Quant.Hybrid provides a PostgreSQL-compatible environment while supporting relational tables, JSON, XML, graph and key-value workloads. Applications can keep familiar drivers and tools while using additional models where they reduce architectural complexity.
Common scenarios
PostgreSQL ecosystem compatibility
Assess SQL dialect, extensions, drivers, administration tools and application behaviour using a representative workload rather than relying only on a feature checklist.
JSON, XML and graph data
Store semi-structured documents, exchange formats and connected data alongside relational entities. A hybrid model can avoid unnecessary synchronisation between separate specialised databases.
Scaling and availability
Horizontal scaling and resilient topologies support growth in data, users and throughput. The target design should include recovery objectives, failure domains, backup and monitoring.
Security-sensitive deployment
Protected editions support Russian operating systems, cryptographic components, hardware tokens and data-at-rest protection. The exact configuration depends on the system category and regulatory requirements.
Migration
Migration from Russian or foreign DBMS platforms begins with inventory: schemas, stored logic, extensions, data volume, peak workload and maintenance windows. A PoC validates compatibility, performance and recovery before the production plan is approved.
Extensibility and operations
Built-in metrics support performance analysis and capacity planning. Extension mechanisms allow the platform to be adapted to application requirements while keeping changes governed.