How the work gets done
Keep the migration in a Microsoft-aware profile, refactor schema and T-SQL for Fabric Warehouse, and surface limitations during assessment. Familiar syntax no longer gets mistaken for compatibility.
Azure SQL Database migration path
Azure SQL Database to Fabric Warehouse
Modernize Azure SQL Database code for Fabric Warehouse while keeping the migration inside the Microsoft data platform.
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 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
A Microsoft-to-Microsoft move still has a target contract
Azure SQL feels close to Fabric because both use T-SQL, but the target is an analytical warehouse with different object support and operating assumptions. Similar syntax can hide different deployment and performance decisions.
Where it helps
Use it to modernize Azure SQL reporting or shared data workloads while preserving a clear inventory of target-ready code and items that need architectural review.
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, constraints, and queries are refactored for the Fabric Warehouse T-SQL surface. Stored procedures and functions are translated where supported, with Fabric-specific limitations called out for review.
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.
Azure SQL schema to Fabric
Refactor tables, views, constraints, and types for Fabric Warehouse while separating portable T-SQL from target-specific definitions.
T-SQL built-ins and conversions
Adapt expressions, date logic, casting, and null behavior where Fabric’s analytical surface changes the result or available syntax.
Identity and generated values
Review identity and default behavior as objects move from Azure SQL’s transactional model into Fabric’s warehouse model.
Procedures and functions
Translate procedural objects with their dependencies and identify which routines need a Fabric-native rewrite or an architectural change.
Target readiness findings
Make Microsoft-to-Microsoft differences visible early, before familiarity creates false confidence in deployment readiness.
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 Fabric Warehouse 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 Fabric Warehouse 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
Fabric Warehouse T-SQL, supported object types, and native alternatives for platform gaps.
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.
Microsoft-to-Microsoft migration profile
Fabric limitation detection
Assessment and generated target code
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.
- Microsoft-to-Microsoft migration path
- Fabric limitation detection
- Assessment and generated target code
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: Fabric Warehouse T-SQL and target-compatible schema definitions.
- 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