How the work gets done
Emit GoogleSQL for relational objects and choose Java, TypeScript, Go, or C# for procedural output. The split gives application owners a clear handoff instead of an ambiguous database rewrite.
Oracle migration path
Oracle to Google Cloud Spanner
Translate Oracle schemas to GoogleSQL and move procedural logic into a selectable application language for Spanner.
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 Oracle schemas, built-ins, data types, dependencies, packages, procedures, functions, and triggers. and producing GoogleSQL for relational objects and a selected application language for procedural behavior.. 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
Spanner requires an explicit boundary between data and behavior
Oracle schemas and PL/SQL rely on a database execution model that does not map directly to Spanner. The risk is not only syntax failure. It is moving business rules out of the database without losing their inputs, dependencies, and expected behavior.
Where it helps
This suits teams modernizing Oracle services into a distributed Spanner-backed application where database code must become maintainable application code.
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
Oracle tables and views become GoogleSQL DDL for the Spanner relational plane. Choose Java, TypeScript, Go, or C# for procedures, functions, triggers, packages, and types that must run outside the database.
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.
Oracle schema to GoogleSQL
Translate relational definitions and queries into GoogleSQL while surfacing differences in types, constraints, and generated values.
PL/SQL to application code
Place procedures, functions, triggers, and packages into a selected Java, TypeScript, Go, or C# output plane rather than pretending Spanner executes PL/SQL.
Sequences and identity behavior
Identify Oracle value-generation assumptions that need application or target-native treatment, then keep them visible in the migration review.
Dependency preservation
Carry relationships from database routines to the generated application surface so engineers can rebuild behavior in a controlled order.
Behavior evidence
Use generated tests to compare important source results and parameters before business rules are moved out of the database.
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 Oracle code. The parser reads every object, builds dependencies, scores complexity, and shows where Google Cloud Spanner 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 Google Cloud Spanner 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
Oracle schemas, built-ins, data types, dependencies, packages, procedures, functions, and triggers.
Target expression
GoogleSQL for relational objects and a selected application language for procedural behavior.
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.
GoogleSQL schema translation
Selectable application-language output
Explicit relational and application-code planes
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.
- GoogleSQL schema translation
- Java, TypeScript, Go, or C# procedural output
- Explicit relational and application-code planes
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: GoogleSQL for relational objects and selected application-language output for procedural behavior.
- 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