How the work gets done
Identify the T-SQL surface, rewrite schema and code for Synapse, and flag distribution, data-type, and feature constraints before the team commits to a target shape.
Azure SQL Database migration path
Azure SQL Database to Azure Synapse Analytics
Refactor Azure SQL Database workloads for an Azure Synapse dedicated SQL pool.
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 Synapse dedicated-pool DDL and T-SQL shaped around distribution and target constraints.. 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
Azure SQL code needs analytical target decisions
Azure SQL workloads commonly mix transactional assumptions with reporting queries, procedures, and schema features. Moving them to a Synapse dedicated pool requires more than changing the connection string because distribution and target feature constraints affect the design.
Where it helps
This path helps teams separate workloads that are ready for analytical scale from code that needs a different execution or data-access strategy.
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 DDL and queries are rewritten for Synapse distribution, data-type, and feature constraints. Stored procedures and functions remain in the T-SQL family while incompatible constructs are refactored or flagged.
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.
Transactional schema to analytical schema
Refactor Azure SQL tables and constraints for a dedicated SQL pool, where distribution and scan patterns influence the target design.
T-SQL and data types
Adapt built-ins, conversions, precision, and feature usage for Synapse while keeping the source behavior available for test comparison.
Procedures and views
Translate shared routines and analytical SQL in dependency order, reducing the risk of fixing a child object before its callers are understood.
Temporary and intermediate state
Identify temporary-table and table-variable patterns that need a target-native implementation rather than a literal copy.
Performance baseline
Use the translated code and generated tests as the starting point for representative Synapse performance checks.
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 Azure Synapse Analytics 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 Azure Synapse Analytics 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
Synapse dedicated-pool DDL and T-SQL shaped around distribution and target constraints.
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-Synapse refactoring
Synapse limitation analysis
Dependency-aware assessment
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-Synapse refactoring
- Synapse limitation analysis
- Dependency-aware assessment
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: Synapse DDL and T-SQL shaped for distribution and target constraints.
- 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