Translate SQL Server schema and T-SQL into the Microsoft Fabric Warehouse dialect, with target limitations surfaced during assessment.
The job to be done
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 Fabric Warehouse T-SQL, supported object types, and native alternatives for platform gaps.. 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
Fabric Warehouse supports familiar T-SQL, but the supported surface and execution behavior are not identical to SQL Server. The dangerous cases are the ones that survive a text conversion and fail only when a procedure, view, or data type meets production-shaped inputs.
How the work gets done
Translate T-SQL with its meaning intact, detect Fabric gaps, and generate correctness tests. The team can review behavior, not just whether the script compiles, while remaining findings stay attached to the object that needs attention.
Where it helps
For a SQL Server estate moving into Fabric, start with the highest-dependency schemas and use the assessment to sequence reporting, shared dimensions, and procedural workloads into manageable waves.
The payoff
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.
SQL Server tables, views, constraints, and queries are refactored for the Fabric Warehouse T-SQL surface. Stored procedures and functions are translated with emulations or explicit limitation markers where Fabric differs from SQL Server.
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
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.
Rewrite SQL Server types such as NVARCHAR, NTEXT, MONEY, and XML into Fabric-compatible representations with the conversion decision visible in output.
Preserve identity behavior where Fabric supports it and adjust non-BIGINT identity definitions so generated values remain usable on the target.
Replace sequence objects with generated backing tables, scalar functions, and procedures that reproduce NEXT VALUE FOR behavior.
Convert table variables into target-appropriate temporary structures, choosing a distributed or MDF-backed approach based on the resulting usage pattern.
Adapt cursors, row-count limiting, error paths, and other T-SQL patterns to Fabric-native equivalents, then exercise them through generated tests.
The path to confidence
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.
Connect or upload SQL Server code. The parser reads every object, builds dependencies, scores complexity, and shows where Fabric Warehouse needs a different construct.
Relational and procedural objects are translated together, with source semantics kept in context. Where Fabric Warehouse has no direct equivalent, the engine uses a supported rewrite or names the gap clearly.
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
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.
SQL Server schemas, T-SQL, built-ins, dependencies, procedures, functions, and triggers.
Fabric Warehouse T-SQL, supported object types, and native alternatives for platform gaps.
Evidence you can use
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.
Your review list
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.
Your working set
Take the next step
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