Polyglot Persistence and Enterprise Data Migration: Architectures, Consistency, and Practice
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I3P106Keywords:
Polyglot Persistence, Database Migration, Change Data Capture, Nosql, Data ConsistencyAbstract
Enterprise data environments increasingly combine relational and non-relational storage systems to satisfy conflicting requirements for consistency, scalability, and query flexibility. This pattern, known as polyglot persistence, pairs traditional relational database management systems with NoSQL stores such as document, key-value, and column-family databases, each handling the workload it serves best. Selecting the right combination of data stores requires weighing consistency guarantees against throughput and latency needs, since no single storage engine satisfies every access pattern efficiently. Parallel to this architectural shift, organizations continue to migrate mission-critical databases from on-premises infrastructure to cloud platforms, a process that introduces risk across security, data integrity, and operational continuity. Change data capture and continuous replication techniques now allow migrations to proceed with near-zero downtime, synchronizing source and target systems throughout the cutover window. Evidence from recent implementations shows that phased, validated migration strategies combined with real-time monitoring substantially reduce data loss and reconciliation errors compared with single-pass batch transfers. Multi-model and hybrid persistence layers further reduce architectural complexity by consolidating several data models within one engine, though trade-offs in performance and operational overhead persist relative to purpose-built single-model systems. Governance frameworks that layer validation, availability, and sustainability metrics on top of migrated systems help organizations sustain data quality after cutover, not merely during it. Together, these developments point toward enterprise data platforms that treat migration and persistence design as a continuum rather than separate disciplines: architectural choices made during migration planning directly determine how well the resulting system supports polyglot workloads, real-time analytics, and regulatory-grade integrity requirements. Practitioners planning large-scale modernization programs benefit from treating schema conversion, replication tooling, and long-term data-store selection as a single coordinated decision rather than sequential, isolated tasks.
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