How the work gets done
Translate T-SQL into PostgreSQL and PL/pgSQL, map types and built-ins, and keep compatibility findings next to the affected objects. Engineers review behavior instead of an undifferentiated script dump.
Azure SQL Database migration path
Azure SQL Database to PostgreSQL
Translate Azure SQL Database schema and T-SQL into PostgreSQL and PL/pgSQL.
The job to be done
Move the work, not just the words.
A migration becomes expensive when every object arrives as a separate mystery. Start with the workload as a whole, then sort the result into three useful categories: what can move directly, what needs adaptation, and what deserves a deliberate engineering decision.
For this path, that means understanding Azure SQL schemas and T-SQL, including the Microsoft-specific assumptions embedded in the workload. and producing PostgreSQL DDL, SQL, and PL/pgSQL that fit the target database model.. The differences that could affect production behavior are surfaced while the team can still act on them, not after they have become deployment surprises.
Why this path is difficult
The small differences are the migration risk
Azure SQL and PostgreSQL both support familiar relational patterns, but built-ins, data types, identity and sequence behavior, procedural code, and null or date semantics differ at the edges where business logic often lives.
Where it helps
Use this scenario for application databases where the target team needs a PostgreSQL code baseline, a focused exception list, and tests that compare important source and target behavior.
The payoff
Know what is ready, what changed, and what still needs a decision.
A real assessment before commitment
The translation runs up front, then becomes an object inventory, coverage view, complexity signal, and findings list. You see the work before you plan it.
Target code that preserves intent
Azure SQL tables, views, data types, constraints, and queries are translated into PostgreSQL-compatible DDL and SQL. T-SQL stored procedures and functions become PL/pgSQL or explicit emulations where PostgreSQL uses a different model.
Evidence for the decisions that remain
Generated tests compare source and target behavior for tables, views, functions, and procedures. Performance is recorded alongside correctness, so a passing translation is not mistaken for a fast one.
Where the work happens
The details that make or break this migration.
These are the source patterns that usually create rework. Each card explains the target-aware treatment, so your team can see what is being adapted and where a decision is still required instead of discovering a mismatch during deployment.
T-SQL expression mapping
Translate date, string, conversion, null, and conditional expressions into PostgreSQL equivalents instead of leaving semantic cleanup to hand review.
Data types and precision
Map Azure SQL types to PostgreSQL types while exposing precision, collation, and implicit-cast decisions that can change application results.
Identity and defaults
Move generated-value behavior into PostgreSQL sequences and defaults while preserving the relationships that inserts and foreign keys depend on.
Procedural conversion
Translate stored procedures and functions into PL/pgSQL with parameters, return behavior, and side effects kept in the object model.
Regression evidence
Generate source-versus-target tests for important result sets and routines so the team can approve behavior with evidence.
The path to confidence
See the work. Make the call. Prove the result.
Assessment, translation, and testing stay connected, so your team can see what the software handled automatically, what it changed to fit the target, and which decisions still need a human owner.
- Assess
Find the true migration surface
Connect or upload Azure SQL Database code. The parser reads every object, builds dependencies, scores complexity, and shows where PostgreSQL needs a different construct.
- Translate
Convert the codebase as a system
Relational and procedural objects are translated together, with source semantics kept in context. Where PostgreSQL has no direct equivalent, the engine uses a supported rewrite or names the gap clearly.
- Validate
Turn output into migration evidence
Deploy the generated target objects to staging, inspect the differences, run generated tests, and use the results to focus engineering time on the exceptions that matter.
The source and target
Make the change easy to explain.
A script that runs is not proof that a migration worked. The real question is what the target needed in order to preserve the workload’s intent, and whether those changes have been tested. SQL Tran keeps that answer visible at the object level.
Source semantics
Azure SQL schemas and T-SQL, including the Microsoft-specific assumptions embedded in the workload.
Target expression
PostgreSQL DDL, SQL, and PL/pgSQL that fit the target database model.
Evidence you can use
Make the decision easy to defend.
This is not a black box. Findings stay attached to the translated workload, so engineers can inspect the change while decision-makers can see what remains before they approve the move.
T-SQL-to-PostgreSQL translation
Data-type and built-in mapping
Assessment-led preview workflow
Your review list
Find the hard parts before they find you.
Every migration has a boundary. Surface it early, and reviewers can spend their time on the few decisions that shape the result instead of rereading thousands of generated lines.
- T-SQL-to-PostgreSQL translation
- Data-type and built-in mapping
- Assessment-led preview workflow
Your working set
What the assessment leaves you with
- An inventory of tables, views, procedures, functions, triggers, and the code volume in each group
- A dependency graph that shows object relationships, unresolved references, and the highest-impact objects to review first
- Generated target output produced by the same translation engine that reports the assessment: PostgreSQL DDL, SQL, and PL/pgSQL shaped for the target database model.
- Object-level findings that identify target gaps, translation errors, and the exact code that needs review
- Behavior tests with expected result sets, changed data, messages, or output values ready for source-versus-target comparison
Take the next step
Replace the guess with an answer from your codebase.
Run the assessment against your objects, dependencies, and target version. We do that by running full translation and exposing the real result. That tells you exactly what translates cleanly, what changes, and what needs your attention.
Start the assessment