Zero data loss. Full auditability. AI-powered migration.
Data migration between NoSQL and SQL platforms is one of the highest-risk operations an enterprise undertakes. Schema mismatches, data type incompatibilities, referential integrity violations, and incremental sync failures compound silently — until production fails.
AIMA 2.0 Migrator applies AI at every stage: schema inference, mapping validation, transformation rule generation, incremental sync management, and reconciliation. Every migration is verifiable, reversible, and documented before any record reaches the target system.
Bidirectional. Schema-aware. Auditable.
NoSQL → SQL
MongoDB, DynamoDB, Cosmos DB to PostgreSQL, MySQL, SQL Server — with schema inference and mapping.
SQL → NoSQL
Relational → document, key-value, or graph stores. Intelligent denormalisation with data integrity checks.
Incremental Sync
Live change capture and incremental sync for continuous migration scenarios with minimal downtime.
Intelligence at every migration layer.
AI Schema Mapping
Automatic schema inference from source data. AI generates candidate mappings and flags ambiguities for human review before migration begins.
Data Validation & Reconciliation
Row-level validation with checksum verification and statistical reconciliation confirms every record migrated correctly before cutover.
Incremental Sync Support
Change data capture keeps source and target in sync during phased migrations — enabling zero-downtime cutovers for production systems.
Full Audit Trail
Every transformation, validation check, and sync operation logged and queryable. Complete lineage from source record to target record.
Rollback Controls
Point-in-time rollback capability at each migration phase. Migration proceeds only when validation gates pass — never silently, never irreversibly.
Performance Tuning
AI optimises batch sizes, parallelism, and throttle rates to maximise migration throughput while respecting source system constraints.
Migration risk is always underestimated. Until it's not.
Most migration failures aren't caused by missing data — they're caused by silent corruption, type coercion errors, and referential integrity violations that only surface in production. AIMA 2.0 catches these before they leave the migration environment, with validation gates that block progress until every check passes.
The result: organisations migrate faster, with less risk, and with a documented proof of correctness that satisfies enterprise audit requirements.