Translate SQL. Don't break it.
SQL Tran maps dependencies, translates with semantic awareness, and generates tests to verify behavior. Start with an assessment to see the supported path, the remaining gaps, and the work required for your codebase. A deterministic core does the translation; optional AI assistance stays under your control.

From the team behind Full Convert and Omni Loader.
How SQL Tran eliminates uncertainty
Cross-platform SQL translation is harder than it looks. Most tools translate syntax and hope for the best. SQL Tran understands what your code actually does, then proves it still does it.
The Real Problems
Why SQL Tran Is Different
Built on semantics, not regex. SQL Tran understands what your code does, how platforms differ, and what the equivalent target code needs to look like.
The static analyzer maps every reference, surfaces every hidden dependency, and feeds the translator and the test generator with the context they need.
Some source features have no direct equivalent on the target. SQL Tran emulates the behavior, preserves the intent, and flags anything that needs your eye.
Generates comprehensive tests automatically. On the Professional plan and higher, it produces hundreds of tests with high coverage. The translation is not done until the tests pass.
How is SQL Tran different?
SQL Tran is the unique tool that truly understands your SQL code and translates it with precision.

Migrations available now
These are the source-to-target combinations exposed by the current SQL Tran project wizard. Start an assessment for your database and version to see what SQL Tran can translate, emulate, test, and flag for review. Preview paths are marked explicitly.
Oracle to PostgreSQL
Translate Oracle schemas and PL/SQL into PostgreSQL, then generate tests that compare source and target behavior.
Explore the scenario →Oracle to Fabric Warehouse
Move Oracle database code toward Microsoft Fabric Warehouse with semantic translation and explicit limitation reporting.
Explore the scenario →Oracle to Fabric Lakehouse
Split an Oracle workload into Spark SQL for relational objects and Python/PySpark for procedural code in Fabric Lakehouse.
Explore the scenario →Oracle to Databricks
Split an Oracle workload into Spark SQL for data objects and Python/PySpark for procedural logic on Databricks.
Explore the scenario →Oracle to Google Cloud Spanner
Translate Oracle schemas to GoogleSQL and move procedural logic into a selectable application language for Spanner.
Explore the scenario →SQL Server to Fabric Warehouse
Translate SQL Server schema and T-SQL into the Microsoft Fabric Warehouse dialect, with target limitations surfaced during assessment.
Explore the scenario →SQL Server to Azure Synapse Analytics
Refactor SQL Server schema and T-SQL for the Azure Synapse dedicated SQL pool surface.
Explore the scenario →SQL Server to PostgreSQL
Translate SQL Server schema and T-SQL into PostgreSQL and PL/pgSQL, with differences surfaced before deployment.
Explore the scenario →Azure SQL Database to Fabric Warehouse
Modernize Azure SQL Database code for Fabric Warehouse while keeping the migration inside the Microsoft data platform.
Explore the scenario →Azure SQL Database to Azure Synapse Analytics
Refactor Azure SQL Database workloads for an Azure Synapse dedicated SQL pool.
Explore the scenario →Azure SQL Database to PostgreSQL
Translate Azure SQL Database schema and T-SQL into PostgreSQL and PL/pgSQL.
Explore the scenario →Azure Synapse Analytics to Fabric Warehouse
Move a Synapse dedicated SQL pool to Fabric Warehouse with focused compatibility analysis and T-SQL refactoring.
Explore the scenario →Frequently asked
questions
Still have questions?
Our team is here to help! Whether you have questions about our products, need assistance with implementation, or want to discuss your specific use case, we’re just a message away.
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