Choose a migration path
See the shape of the work before you start.
Each scenario follows the paths represented in the SQL Tran project wizard. Use the filters to
narrow the catalog, then open a scenario for the target-specific translation profile,
emulation choices, workflow, and review points.
GA SQL Tran
Oracle → PostgreSQL
Translate Oracle schemas and PL/SQL into PostgreSQL, then generate tests that compare source and target behavior.
Relational output
Oracle tables, views, data types, constraints, and SQL become PostgreSQL-compatible DDL and queries.
Procedural output
PL/SQL packages, procedures, functions, and triggers become PL/pgSQL or explicit emulations where PostgreSQL needs a different construct.
- Dependency-aware PL/SQL translation
- Oracle built-in and data-type mapping
- Generated source-versus-target correctness tests
View scenario details →Preview SQL Tran
Oracle → Fabric Warehouse
Move Oracle database code toward Microsoft Fabric Warehouse with semantic translation and explicit limitation reporting.
Relational output
Oracle tables, views, types, and queries are translated into the T-SQL surface supported by Fabric Warehouse.
Procedural output
PL/SQL objects are translated where Fabric has a viable equivalent; unsupported target behavior is tagged for review instead of hidden.
- Oracle-to-Fabric translation profile
- Fabric limitation detection
- Assessment before target deployment
View scenario details →Preview SQL Tran
Oracle → Fabric Lakehouse
Split an Oracle workload into Spark SQL for relational objects and Python/PySpark for procedural code in Fabric Lakehouse.
Relational output
Oracle tables and views are emitted as Spark SQL for the Fabric Lakehouse relational plane.
Procedural output
Procedures, functions, triggers, packages, and types are emitted as Python/PySpark code for the execution plane.
- Hybrid SQL-and-code target profile
- Spark SQL relational output
- Python/PySpark procedural output
View scenario details →Preview SQL Tran
Oracle → Databricks
Split an Oracle workload into Spark SQL for data objects and Python/PySpark for procedural logic on Databricks.
Relational output
Oracle tables and views are translated into Spark SQL for the Databricks relational plane.
Procedural output
Procedures, functions, triggers, packages, and types are translated into Python/PySpark for the code plane.
- Hybrid SQL-and-code target profile
- Spark SQL relational output
- Python/PySpark procedural output
View scenario details →Preview SQL Tran
Oracle → Google Cloud Spanner
Translate Oracle schemas to GoogleSQL and move procedural logic into a selectable application language for Spanner.
Relational output
Oracle tables and views become GoogleSQL DDL for the Spanner relational plane.
Procedural output
Choose Java, TypeScript, Go, or C# for procedures, functions, triggers, packages, and types that must run outside the database.
- GoogleSQL schema translation
- Java, TypeScript, Go, or C# procedural output
- Explicit relational and application-code planes
View scenario details →GA SQL Tran
SQL Server → Fabric Warehouse
Translate SQL Server schema and T-SQL into the Microsoft Fabric Warehouse dialect, with target limitations surfaced during assessment.
Relational output
SQL Server tables, views, constraints, and queries are refactored for the Fabric Warehouse T-SQL surface.
Procedural output
Stored procedures and functions are translated with emulations or explicit limitation markers where Fabric differs from SQL Server.
- T-SQL-aware Fabric refactoring
- Fabric limitation detection
- Generated correctness tests
View scenario details →Preview SQL Tran
SQL Server → Fabric Lakehouse
Translate SQL Server tables and queries to Spark SQL, and move stored procedures and functions into PySpark for Fabric Lakehouse.
Relational output
SQL Server tables, views, and queries become Spark SQL for the Fabric Lakehouse relational plane.
Procedural output
T-SQL procedures, functions, triggers, and procedural workflows become PySpark code for the Spark execution plane.
- SQL Server-to-Spark SQL translation
- T-SQL-to-PySpark procedural output
- Generated source-versus-target tests
View scenario details →GA SQL Tran
SQL Server → Azure Synapse Analytics
Refactor SQL Server schema and T-SQL for the Azure Synapse dedicated SQL pool surface.
Relational output
SQL Server DDL and queries are rewritten for Synapse distribution, data-type, and T-SQL constraints.
Procedural output
Stored procedures and functions stay in the T-SQL family while SQL Tran refactors constructs Synapse handles differently.
- T-SQL-to-Synapse refactoring
- Synapse limitation analysis
- Dependency-aware migration assessment
View scenario details →GA SQL Tran
SQL Server → PostgreSQL
Translate SQL Server schema and T-SQL into PostgreSQL and PL/pgSQL, with differences surfaced before deployment.
Relational output
SQL Server tables, views, data types, constraints, and queries are translated into PostgreSQL DDL and SQL.
Procedural output
T-SQL stored procedures and functions become PL/pgSQL or explicit emulations where the database models differ.
- T-SQL-to-PostgreSQL translation
- Data-type and built-in mapping
- Explicit gaps instead of silent rewrites
View scenario details →GA SQL Tran
Azure SQL Database → Fabric Warehouse
Modernize Azure SQL Database code for Fabric Warehouse while keeping the migration inside the Microsoft data platform.
Relational output
Azure SQL tables, views, constraints, and queries are refactored for the Fabric Warehouse T-SQL surface.
Procedural output
Stored procedures and functions are translated where supported, with Fabric-specific limitations called out for review.
- Microsoft-to-Microsoft migration path
- Fabric limitation detection
- Assessment and generated target code
View scenario details →GA SQL Tran
Azure SQL Database → Azure Synapse Analytics
Refactor Azure SQL Database workloads for an Azure Synapse dedicated SQL pool.
Relational output
Azure SQL DDL and queries are rewritten for Synapse distribution, data-type, and feature constraints.
Procedural output
Stored procedures and functions remain in the T-SQL family while incompatible constructs are refactored or flagged.
- T-SQL-to-Synapse refactoring
- Synapse limitation analysis
- Dependency-aware assessment
View scenario details →Preview SQL Tran
Azure SQL Database → Fabric Lakehouse
Translate Azure SQL tables and queries to Spark SQL, and move procedural T-SQL into PySpark for Fabric Lakehouse.
Relational output
Azure SQL tables, views, and queries become Spark SQL for the Fabric Lakehouse relational plane.
Procedural output
T-SQL procedures, functions, triggers, and transformation workflows become PySpark code for the Spark execution plane.
- Azure SQL-to-Spark SQL translation
- T-SQL-to-PySpark procedural output
- Generated cross-plane behavior tests
View scenario details →GA SQL Tran
Azure SQL Database → PostgreSQL
Translate Azure SQL Database schema and T-SQL into PostgreSQL and PL/pgSQL.
Relational output
Azure SQL tables, views, data types, constraints, and queries are translated into PostgreSQL-compatible DDL and SQL.
Procedural output
T-SQL stored procedures and functions become PL/pgSQL or explicit emulations where PostgreSQL uses a different model.
- T-SQL-to-PostgreSQL translation
- Data-type and built-in mapping
- Assessment-led preview workflow
View scenario details →GA SQL Tran
Azure Synapse Analytics → Fabric Warehouse
Move a Synapse dedicated SQL pool to Fabric Warehouse with focused compatibility analysis and T-SQL refactoring.
Relational output
Synapse tables, views, materialized-view patterns, and queries are refactored for Fabric Warehouse.
Procedural output
Synapse procedures are translated within the T-SQL family, with unsupported Fabric constructs tagged for review.
- Synapse-to-Fabric compatibility analysis
- T-SQL-aware refactoring
- Generated validation workflow
View scenario details →