Oracle to PostgreSQL
Migrate Oracle workloads to PostgreSQL without losing behavior, logic, or control. Even the most complex PL/SQL code is automatically converted and emulated in PostgreSQL.
This is not syntax conversion.
This is behavioral preservation.
Oracle-to-PostgreSQL migrations fail when teams underestimate how deeply Oracle-specific their systems really are. SQL Tran was built specifically to handle that reality. It understands Oracle semantics at scale and translates them into PostgreSQL equivalents, automatically, repeatably, and safely.
Packages, views, functions procedures, object types, complex transactions
Advanced and highly optimized PL/SQL constructs
Database-resident business logic accumulated over decades
PostgreSQL is powerful - but it is not Oracle
Manual rewrites inevitably introduce regressions, delays, and risk. Most tools stop at surface-level translation and that’s why migrations are infamous for being a high-risk and expensive endeavor.
SQL Tran was designed for complex Oracle codebases
It uses deep static analysis and a large, ever-growing emulation layer to bridge the gap between Oracle and PostgreSQL. It includes hundreds of built-in emulations, covering both common and deeply obscure Oracle features, including:
SQL Tran doesn’t just rewrite logic. It transforms it into idiomatic Postgres code that behaves exactly the same. SQL Tran parses and analyzes every object:
Oracle has rich and complex type system, and SQL Tran handles it all, from simple types to the most complex user-defined ones.
Table definitions are properly analyzed and translated, including complex partitioning schemes.
Even the most complex views are translated correctly. For example, old-style join syntax is properly handled and converted to modern explicit ones.
Procedural code is deeply analyzed, and translated to equivalent Postgres logic, using emulation where necessary to ensure identical behavior. Even nested procedures are optimally rewritten.
Scalar, table-valued, and aggregate functions are fully supported.
Triggers are translated with full fidelity, including complex firing rules and mutating table access.
Nothing is treated in isolation
Dependencies across schemas are analyzed and a full object dependency map is built. Broken references are located and reported, whether they are result of missing objects in your SQL script or dropped objects breaking your database code.
Oracle constructs are translated with full awareness of intent, not just syntax.
- ● Thanks to a super-fast and fully custom parsers, translation of even the largest codebases is practically instant.
- ● Packages are decomposed safely
- ● Oracle-only features are mapped or emulated
- ● PostgreSQL-native constructs are used where appropriate
- ● Assessment is instant and detailed – a report of a complete translation with deep statistics.
SQL Tran's powerful testing framework is the ultimate project enabler:
- ● Even if all you have is a SQL schema dump you can run correctness and performance testing
- ● Generate synthetic test data tailored to your source and target schema
- ● Ensure functions return the same results
- ● Ensure procedures affect tables identically
- ● Ensure edge cases and emulations are handled correctly
Trust is earned, not assumed.
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 →Get SQL Tran today!
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