SQL Tran scenarios

Find your migration path

Start with the source and target you actually have. Review what SQL Tran produces for relational and procedural code, see whether a path is available or in preview, then open the detailed scenario before assessing your own codebase.

Explore scenarios Assess your codebase
14
source-to-target paths
8
GA paths
4
source platforms covered

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.

Show Showing 14 of 14
Oracle PostgreSQL
GA

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
SQL Server Fabric Warehouse
GA

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
SQL Server Azure Synapse Analytics
GA

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
SQL Server PostgreSQL
GA

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
Azure SQL Database Fabric Warehouse
GA

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
Azure SQL Database Azure Synapse Analytics
GA

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
Azure SQL Database PostgreSQL
GA

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
Azure Synapse Analytics Fabric Warehouse
GA

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
Oracle Fabric Warehouse
Preview

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
Oracle Fabric Lakehouse
Preview

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
Oracle Databricks
Preview

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
Oracle Google Cloud Spanner
Preview

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
SQL Server Fabric Lakehouse
Preview

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
Azure SQL Database Fabric Lakehouse
Preview

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

From path to proof

A scenario is the start of an evidence-led migration.

The scenario pages make the target model explicit before you put a real codebase through the workflow. They explain the output planes, the target differences that need attention, and the validation steps used to review the translated result.

Choose the closest path

Match your current database and intended target. GA and preview labels make the product status visible before you go further.

Understand the output

See whether the path stays in SQL or separates relational objects from procedural code such as PySpark or application-language output.

Assess your codebase

Use the selected scenario as a shared starting point, then let SQL Tran measure the objects, dependencies, target limitations, and correctness work in your own project.

Get SQL Tran today!

Ready to get started?

Request a Demo