Data Migration
Oracle to PostgreSQL migration: a practical guide
8 min read · Guide by humaineeti
Migrating from Oracle to PostgreSQL is one of the most common — and most underestimated — database modernization projects. Migrations are won and lost in the discovery phase, long before a single object is moved. This guide covers the real challenges, a sensible sequence, cutover strategies, and how agentic AI is changing the discovery and planning work.
Start with discovery, not conversion
The biggest mistake is rushing to convert schema before understanding what you have. A proper discovery phase inventories every Oracle instance, version, and feature in use, and builds a detailed picture: object counts by schema and type, data sizes, and the lines of code in each package, function, and procedure. The blockers that derail timelines — undocumented stored procedures, cyclic dependencies, orphaned data, permission drift — are cheap to find here and expensive to find later.
The three technical challenges
Most of the real work concentrates in three areas. Data-type mapping is subtle: Oracle's NUMBER, RAW, and CLOB have no exact PostgreSQL equivalents, and NUMBER-to-NUMERIC or DATE/TIMESTAMP differences can quietly change results. PL/SQL conversion — especially packages and autonomous transactions — needs genuine rework, not a find-and-replace. And identifier casing differs: Oracle upper-cases unquoted names while PostgreSQL lower-cases them, so inconsistent quoting causes surprises.
- Data types — NUMBER, RAW, CLOB, DATE/TIMESTAMP precision
- PL/SQL — packages, triggers, and autonomous transactions
- Identifiers — Oracle upper-cases, PostgreSQL lower-cases unquoted names
Choosing a cutover strategy
Two approaches dominate. A big-bang cutover moves schema and data once and switches everything together — simpler and viable for smaller databases with a maintenance window. A phased approach with change data capture (CDC) keeps Oracle and PostgreSQL in sync through a validation period, then cuts over by switching the application's connection string — essential for systems that cannot go offline. Match the strategy to your downtime tolerance, not the other way around.
How agentic AI de-risks the work
The discovery and planning phases are exactly where AI agents now add the most value. Agents can introspect a source system into a queryable metadata graph, grade it against best practices to surface hidden issues, and generate a sequenced migration plan with dialect translations, risk levels, and rollback paths — read-only, so the source is never touched. The human DBA still reviews and executes; agents advise, people decide.
Don't forget compliance and validation
A modernization is also a chance to clean up governance. Encode PII detection and regulatory requirements (such as the DPDP Act and GDPR) as checks in the plan, document which permissions carry forward, and build validation checkpoints into every step. The goal is a detailed work-breakdown the DBA can execute against a known-good map — not a leap of faith into production.
Related accelerator
RekonAIDe — Data Migration
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Common questions
What are the hardest parts of Oracle to PostgreSQL migration?+
Data-type mapping (NUMBER, RAW, CLOB, DATE precision), PL/SQL conversion (packages and autonomous transactions), and identifier casing differences — all of which should be surfaced in the discovery phase before any cutover.
Should I use a big-bang or phased migration?+
Big-bang suits smaller databases with a maintenance window; a phased approach with change data capture keeps both databases in sync and is required for systems that cannot go offline. Choose based on downtime tolerance.
How does AI help with database migration?+
Agentic AI automates the discovery and planning phase: it scans the source read-only into a metadata graph, grades it against best practices, and produces a sequenced plan with risk levels and rollback paths — while the DBA reviews and executes.
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