SQL: any dialect → any dialect
Function aliases, type differences, LIMIT/TOP/ROWNUM, upsert syntax, JSON operators, quoting rules — every conversion comes with an itemized change list and semantic warnings.
SQL is a standard the way music is a standard. PostgreSQL, MySQL, SQLite, SQL Server, Oracle, BigQuery, and Snowflake each implement the spec with their own dialect — different function names, different quote rules, different type systems, different limits on correlated subqueries. Migrating a query that runs perfectly in one is rarely a copy-paste job into another.
Qaynaq Labs' SQL translator handles common conversions between the seven major dialects: function aliases (NOW vs CURRENT_TIMESTAMP vs SYSDATE), data types (SERIAL vs IDENTITY vs AUTO_INCREMENT), result limiting (LIMIT vs TOP vs ROWNUM vs FETCH FIRST), upserts (ON CONFLICT vs ON DUPLICATE KEY vs MERGE), JSON operators, and quoting rules. Every change is itemized with a reason, and semantic warnings flag any case where the target dialect's behavior may differ at runtime.
Frequently asked
Does it preserve query semantics?
We try to. When something can't be preserved exactly (e.g., implicit type coercion differs across dialects), the result includes a semantic warning flagging it.
Will it handle stored procedures?
It handles single SELECT/INSERT/UPDATE/DELETE/CREATE statements well. Complex procedural code (stored procedures, triggers) is on the roadmap.
What about DDL?
DDL is supported — type maps and constraint syntax differ significantly across dialects and the translator handles the common cases (column types, constraints, indexes, generated columns).
Does it warn about performance differences?
Yes — when a translation lands on a syntactically valid query that may perform very differently (missing index hints, dialect-specific optimization opportunities), the tool flags it in performance notes.