Assess Migration Complexity Before Choosing AI Tools
Database migrations tolerate three states at once: the upgrade path, the downgrade path, and the awkward middle of dual writes, ghost tables, and background back-fills. Edge cases multiply when real data arrives.
Start with an explicit complexity assessment before choosing any AI tool. A move from one SQL engine to another is not just a syntax change; it shifts consistency, indexing, and transaction boundaries. Team familiarity is only one factor in choosing the target engine; the workload may fit one engine far better than the other. AI translators do not remove that decision. Inventory the objects that are not plain DDL: triggers, stored procedures, scheduled jobs, and data types with engine-specific behavior. These are where a rule-based conversion is most likely to miss a semantic difference.
AWS DMS Schema Conversion with generative AI supports Oracle to PostgreSQL or Aurora PostgreSQL, SQL Server to PostgreSQL or Aurora PostgreSQL, and SAP ASE to PostgreSQL. The tool can convert an entire schema or a single object. The generative AI feature offers recommendations for previously unconverted code objects that typically require complex manual conversion; all other SQL elements are converted using default rule-based logic. The documentation still lists unsupported constructs that remain manual action items. Read that list before writing the first translated DDL, not after cutover.
Convert Schema and Queries with Human Oversight
Generative AI produces the fastest wins in schema conversion. Use it to generate DDL for tables, indexes, and constraints, then compare every object against the original semantics. For each recommendation, review the proposed code against the original before applying it. The feature has explicit limits. MERGE statements with filtering conditions, hierarchical queries with pseudocolumns, CURSOR expressions, and system objects remain action items or internal errors. The model does not make those constructs disappear.
Target engines differ in join syntax, transaction semantics, and type behavior, so AI can return SQL that parses cleanly but changes meaning. Treat every translated query as a hypothesis. Compare query plans and result sets, not just syntax. Let the model propose, and keep a human responsible for every translated object.
Validate with Synthetic Data and Automated Tests
Validation starts before cutover with synthetic data. Real production data is too messy to exercise every branch, so generate rows that mirror your distribution and then add outliers. If a schema change splits a name column into firstname and lastname, include emojis, empty strings, values at the length limit, nulls, and duplicate keys. Those inputs expose inconsistent behavior and rollback chaos.
Automate the comparison of query results between source and target engines. Correctness tests should check row counts, ordering, null handling, and type coercion. Performance tests belong in the same suite. A query can return identical rows and still be too slow because it misses an index or uses a different join order. The migration is validated only when the target engine returns the same answers with acceptable latency. Run these comparisons in the same automated pipeline that runs the rest of your test suite. Validation must cover data integrity and edge-case behavior, not merely whether the queries parse.
Plan Rollback and Use Feature Flags to Reduce Risk
Rollback is part of the migration design, not a contingency. Feature flags let you shift traffic gradually and reverse quickly if validation fails. If a converted query returns wrong results, the flag routes users back to the old engine while the translation is fixed.
AI handles mechanical translation, but it does not decide when to abort. Define the trigger conditions and the person who owns the call before the first cutover attempt. Rehearse the rollback procedure under load. Schedule the drill before the cutover window, not during it. Never treat cutover as final.
