Oracle to Postgres Conversion Using Newt Global DMAP™

A Deep Technical Dive into Enterprise-Scale Database Modernization

Oracle to Postgres conversion is a complex, engineering-intensive transformation that goes far beyond schema translation. It requires rethinking database architecture, refactoring PL/SQL-heavy logic, redesigning performance strategies, and aligning the target PostgreSQL platform with cloud-native principles.

This blog takes a technical deep dive into Oracle to PostgreSQL migration, enriched with real conversion examples, DMAP™ architectural diagrams, and engineering-level details on how Newt Global’s DMAP™ (Discovery, Migration, and Automation Platform) systematically de-risks and accelerates large-scale Oracle modernization programs.

What Oracle to Postgres Conversion Really Means

From a technical standpoint, Oracle to Postgres conversion is the transformation of:

• Oracle physical and logical schemas

• Proprietary Oracle data types and constraints

• PL/SQL procedural logic and packages

• Oracle-specific SQL constructs and optimizations

• Data movement and synchronization mechanisms

into PostgreSQL-compatible, cloud-optimized equivalents, while ensuring transactional consistency, functional parity, and performance equivalence.

Core Architectural Differences That Drive Migration Complexity

Understanding architectural mismatches is essential before conversion begins.

These differences are the primary reason Oracle to PostgreSQL migration cannot be treated as a lift-and-shift exercise.

DMAP™ Architecture for Oracle to PostgreSQL Migration

DMAP™ End-to-End Migration Architecture

┌──────────────────────────────────┐
│        Oracle Environment         │
│ • Schemas / Tables / Indexes      │
│ • PL/SQL Packages & Procedures    │
│ • Triggers / Views / Sequences    │
│ • Application SQL                 │
└───────────────┬──────────────────┘
                │
                ▼
┌──────────────────────────────────┐
│          DMAP™ Discover           │
│ • Object Inventory                │
│ • Dependency Graphs               │
│ • PL/SQL Complexity Scoring       │
│ • PostgreSQL Compatibility Index │
└───────────────┬──────────────────┘
                │
                ▼
┌──────────────────────────────────┐
│          DMAP™ Migrate            │
│ • Automated DDL Conversion        │
│ • PL/SQL → PL/pgSQL Mapping       │
│ • Data Migration Pipelines        │
│ • Exception Identification        │
└───────────────┬──────────────────┘
                │
                ▼
┌──────────────────────────────────┐
│          DMAP™ Automate           │
│ • SQL Rewrite Optimization        │
│ • Validation Framework            │
│ • Performance Engineering         │
│ • Cloud Enablement                │
└───────────────┬──────────────────┘
                │
                ▼
┌──────────────────────────────────┐
│        PostgreSQL Target           │
│ • Cloud / Hybrid / On-Prem        │
│ • Optimized Schemas & Indexes     │
│ • Tuned Queries & Connections     │
└──────────────────────────────────┘

Read 2 Mins: Oracle to PostgreSQL Migration in Weeks with DMAP AI

Discovery & Complexity Analysis

Example: PL/SQL Complexity Scoring

Oracle PL/SQL packages often contain:

• Nested cursors

• Autonomous transactions

• Dynamic SQL

• Exception-driven control flow

DMAP™ Discover parses and scores code complexity.

Example PL/SQL Pattern

FOR rec IN (SELECT * FROM orders WHERE status = 'NEW') LOOP
  BEGIN
    process_order(rec.order_id);
  EXCEPTION
    WHEN OTHERS THEN
      log_error(SQLERRM);
  END;
END LOOP;

DMAP™ identifies:

• Cursor loop → convertible

• Exception block → requires refactoring

• Implicit commit behavior → risk

This early insight prevents late-stage migration surprises.

Schema Conversion with Examples

Oracle to PostgreSQL Data Type Mapping

Oracle Table

CREATE TABLE customers (
  customer_id NUMBER(10),
  customer_name VARCHAR2(100),
  created_date DATE
);

PostgreSQL Equivalent

CREATE TABLE customers (
  customer_id BIGINT,
  customer_name VARCHAR(100),
  created_date TIMESTAMP
);

DMAP™ automatically:

• Converts NUMBER to optimal numeric types

• Replaces DATE with TIMESTAMP

• Flags precision and scale risks

PL/SQL to PL/pgSQL Conversion

Example: Stored Procedure Conversion

Oracle PL/SQL Procedure

CREATE OR REPLACE PROCEDURE update_salary (
  p_emp_id IN NUMBER,
  p_increment IN NUMBER
) AS
BEGIN
  UPDATE employees
  SET salary = salary + p_increment
  WHERE emp_id = p_emp_id;
  COMMIT;
END;

PostgreSQL PL/pgSQL Function

CREATE OR REPLACE FUNCTION update_salary (
  p_emp_id BIGINT,
  p_increment NUMERIC
)
RETURNS VOID AS $$
BEGIN
  UPDATE employees
  SET salary = salary + p_increment
  WHERE emp_id = p_emp_id;
END;
$$ LANGUAGE plpgsql;

Key Technical Changes

• Procedures → Functions

• Explicit commits removed

• Transaction control handled externally

DMAP™ automatically flags Oracle commit semantics for remediation.

Data Migration & CDC Flow

Migration Flow (Zero Downtime)

Oracle Source Tables
   │
   ├── Initial Full Load
   │
   ├── Parallel Chunking (by PK ranges)
   │
   ├── Change Data Capture (Redo Logs)
   │
   └── Continuous Sync
           │
           ▼
    PostgreSQL Target Tables

DMAP™ ensures:

• Referential integrity validation

• Row count and checksum comparison

• Automated reconciliation reports

SQL Rewrite & Query Optimization

Example: Pagination Conversion

Oracle SQL

SELECT * FROM (
  SELECT a.*, ROWNUM rnum
  FROM orders a
  WHERE ROWNUM <= 50
)
WHERE rnum > 25;

PostgreSQL SQL

SELECT *
FROM orders
ORDER BY order_id
LIMIT 25 OFFSET 25;

DMAP™:

• Rewrites pagination logic

• Removes Oracle hints

• Aligns queries with PostgreSQL planner behavior

Performance Engineering

Index Strategy Example

Oracle

• Bitmap indexes on low-cardinality columns

PostgreSQL Replacement

• B-tree or GIN indexes

• BRIN indexes for large time-series tables

Example

CREATE INDEX idx_orders_status
ON orders USING GIN (status);

DMAP™ validates:

• Query execution plans

• Index selectivity

• Memory and connection settings

Read 2 Mins: Top Benefits of Using AI for Cloud Migration and Legacy-to-Cloud Modernization

Common Oracle to Postgres Conversion Pitfalls

Why DMAP™ Works for Complex Oracle Migrations

DMAP™ succeeds where traditional tools fail because it:

• Shifts complexity discovery to day one

• Uses automation with engineering oversight

• Provides object-level traceability

• Aligns PostgreSQL with cloud-native patterns

This enables predictable Oracle to PostgreSQL migration outcomes at enterprise scale.

Conclusion

Oracle to Postgres conversion is a deep engineering transformation—not a simple database switch.
With Newt Global’s DMAP™, enterprises gain a technically rigorous, AI-driven migration framework that addresses schema conversion, PL/SQL refactoring, data migration, SQL optimization, and performance engineering in a unified, controlled manner.

For organizations modernizing Oracle estates at scale, DMAP™ delivers the precision, automation, and confidence required to succeed in cloud-first architectures.

Frequently Asked Questions

What is Oracle to Postgres conversion?

Oracle to Postgres conversion is the process of migrating Oracle database schemas, data, PL/SQL code, and SQL queries to PostgreSQL while ensuring functional equivalence, performance optimization, and cloud readiness.

Why do enterprises migrate from Oracle to PostgreSQL?

Enterprises migrate from Oracle to PostgreSQL to reduce licensing costs, eliminate vendor lock-in, improve cloud portability, and adopt an open-source, enterprise-grade database platform supported across AWS, Azure, and Google Cloud.

What are the key challenges in Oracle to PostgreSQL migration?

The primary challenges in Oracle to PostgreSQL migration include PL/SQL package conversion, Oracle-specific SQL features, schema incompatibilities, performance tuning requirements, and application dependency remediation.

How does Newt Global DMAP™ reduce migration risk?

Newt Global DMAP™ reduces migration risk through AI-driven discovery, automated schema and code conversion, predictive complexity analysis, and performance engineering—delivering predictable and controlled Oracle to PostgreSQL migration outcomes.

Can PL/SQL be converted to PL/pgSQL automatically?

PL/SQL can be partially automated during conversion; however, complex constructs such as packages, autonomous transactions, and dynamic SQL require refactoring. DMAP™ identifies auto-convertible logic and provides remediation guidance for unsupported patterns.

How is Oracle data migrated to PostgreSQL?

Oracle data is migrated to PostgreSQL using bulk data loads, parallel migration techniques, and Change Data Capture (CDC) to enable near-zero downtime while maintaining data integrity and validation.

Does PostgreSQL provide performance comparable to Oracle?

Yes. PostgreSQL can deliver performance comparable to Oracle when schemas, indexes, queries, and connection management are optimized for PostgreSQL execution models and cloud infrastructure.

How long does an Oracle to Postgres conversion take?

Oracle to Postgres conversion timelines typically range from a few weeks to several months, depending on database size, PL/SQL complexity, data volume, and downtime requirements.

Which cloud platforms support PostgreSQL after migration?

PostgreSQL is supported on major cloud platforms including AWS (RDS and Aurora PostgreSQL), Microsoft Azure Database for PostgreSQL, Google Cloud SQL, as well as hybrid and on-prem environments.

Is Oracle to PostgreSQL migration suitable for mission-critical systems?

Yes. With proper discovery, automation, validation, and performance engineering—such as that provided by Newt Global DMAP™—Oracle to PostgreSQL migration is suitable for mission-critical enterprise workloads.