How the work gets done
Translate T-SQL into PostgreSQL and PL/pgSQL with data-type and built-in mapping. Findings call out the constructs that need emulation or redesign, and generated tests prove the cases that matter.
SQL Server migration path
SQL Server to PostgreSQL
Translate SQL Server schema and T-SQL into PostgreSQL and PL/pgSQL, with differences surfaced before deployment.
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 SQL Server schemas, T-SQL, built-ins, dependencies, procedures, functions, and triggers. 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
Crossing database families exposes hidden T-SQL assumptions
SQL Server workloads often depend on T-SQL built-ins, identity behavior, date and string semantics, procedural control flow, and SQL Server-specific object features. A mechanical rewrite can remove the syntax while leaving a different result or failure mode.
Where it helps
This is useful for teams moving an application or service from SQL Server to PostgreSQL while keeping a reviewable record of what was converted, adapted, and left for engineering decision.
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
SQL Server tables, views, data types, constraints, and queries are translated into PostgreSQL DDL and SQL. T-SQL stored procedures and functions become PL/pgSQL or explicit emulations where the database models differ.
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 built-ins
Map SQL Server string, date, conversion, null, and error-handling functions to PostgreSQL expressions while preserving the original intent.
Identity and sequences
Translate identity-backed value generation into PostgreSQL sequence and default patterns, keeping dependent inserts and references aligned.
Procedures and functions
Convert T-SQL control flow, parameters, return values, and side effects into PL/pgSQL routines with explicit findings for different semantics.
Views and joins
Rewrite SQL Server-specific query patterns and preserve dependencies so views are validated against the same representative result sets.
Triggers and transaction behavior
Carry trigger logic and transactional assumptions into target routines, then use generated tests to catch changes at the edge of the workflow.
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 SQL Server 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
SQL Server schemas, T-SQL, built-ins, dependencies, procedures, functions, and triggers.
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
Explicit gaps instead of silent rewrites
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
- Explicit gaps instead of silent rewrites
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