How the work gets done
Build a dependency-aware model, map Oracle types and built-ins, and turn packages and procedural objects into PL/pgSQL where possible. Emulation and redesign decisions stay visible in the generated result.
Oracle migration path
Oracle to PostgreSQL
Translate Oracle schemas and PL/SQL into PostgreSQL, then generate tests that compare source and target behavior.
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 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
Oracle behavior is concentrated in the details
The hard part is rarely table DDL. It is the interaction between Oracle built-ins, implicit conversions, package state, exception paths, sequences, triggers, and dependency order. A script that looks translated can still behave differently when a production procedure reaches an uncommon branch.
Where it helps
A finance or operations schema with packages, reporting views, triggers, and shared utility functions can be assessed as one system. Reviewers can start with the objects that affect the most dependents.
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, views, data types, constraints, and SQL become PostgreSQL-compatible DDL and queries. PL/SQL packages, procedures, functions, and triggers become PL/pgSQL or explicit emulations where PostgreSQL needs a different construct.
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 types and conversions
Map Oracle numeric, date, character, large-object, and user-defined types into PostgreSQL equivalents while keeping precision and conversion behavior visible for testing.
Built-ins and expressions
Translate Oracle-specific functions, null handling, string operations, date arithmetic, and old-style joins into PostgreSQL expressions instead of leaving a search-and-replace cleanup for the team.
Packages and nested procedures
Break package-contained logic into PostgreSQL-compatible routines, preserve call relationships, and keep nested procedure behavior in the dependency model.
Sequences, triggers, and defaults
Carry value generation, trigger firing, default expressions, and dependent object order into the target schema so inserts and side effects are tested together.
Partitions, views, and complex SQL
Translate partition definitions and deeply nested views while preserving references, then generate source-versus-target tests for the result sets that matter.
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 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
Oracle schemas, built-ins, data types, dependencies, packages, 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.
Dependency-aware PL/SQL translation
Oracle type and built-in mapping
Generated source-versus-target behavior tests
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.
- Dependency-aware PL/SQL translation
- Oracle built-in and data-type mapping
- Generated source-versus-target correctness tests
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