From Oracle to Amazon RDS PostgreSQL: A Migration and Translation Guide

• Assess: Run the AWS Schema Conversion Tool (SCT) assessment against the Oracle source to produce a report of what converts automatically and what needs manual work, keeping in mind that the usual offenders are PL/SQL packages, triggers, optimiser hints, and Oracle-specific data types. 

• Convert the schema: Use AWS SCT to convert tables, views, sequences, and PL/SQL into their PostgreSQL equivalents in PL/pgSQL, then expect to hand-fix anything SCT flags: packages become schemas of functions, hint-driven SQL is rewritten, and a few data types need deliberate mapping. 

• Migrate the data: Use AWS DMS with a full load followed by CDC (change data capture, or ongoing replication) so the Oracle source keeps serving traffic while data streams into the RDS for PostgreSQL target, splitting very large tables partition by partition to keep the job restartable, support parallel processing, and give you per-partition progress visibility. 

• Validate: Reconcile row counts and checksums using DMS data validation, and run application smoke tests against the RDS copy before anyone depends on it. 

• Cutover: Stop writes on Oracle, let CDC drain the final changes into RDS PostgreSQL, and then repoint the application at the RDS endpoint, ideally through RDS Proxy or a PgBouncer pool for the reasons set out below. 

• Operate as a DBRE: Once you are live, the work shifts to autovacuum and bloat monitoring, connection pooling, Parameter Groups, Performance Insights, PITR, and backups, while AWS handles Multi-AZ failover. 

The Paradigm Shifts Every Oracle DBA Must Master 

Moving to Amazon RDS for PostgreSQL means shifting a few deeply ingrained habits to keep the system stable and performant. Where Oracle updates blocks in place and parks old data in Undo Tablespaces, PostgreSQL uses Multi-Version Concurrency Control (MVCC), writing a new version of each row and leaving the old one behind as a dead tuple; those dead tuples are cleaned up asynchronously by VACUUM, so you watch pg_stat_user_tables to make sure bloat does not quietly degrade your index scans. 

Connection handling changes in a similar way because while Oracle absorbs thousands of sessions comfortably through dedicated and shared server architectures, PostgreSQL allocates a dedicated OS process per connection, making a connection pooler such as RDS Proxy or PgBouncer essential to avoid severe context-switching overhead on a busy instance. 

Optimiser behaviour requires the third adjustment: Oracle DBAs lean on explicit hints like /*+ INDEX */ when statistics drift, but the core PostgreSQL engine does not support hints, meaning you guide the planner instead by running ANALYZE regularly, tuning parameters like random_page_cost and effective_cache_size, and enabling the pg_hint_plan extension only as an emergency fallback. 

That same theme carries into how you spend your day. With AWS managing hardware, storage scaling, automated backups, and Multi-AZ failover, your attention moves from infrastructure upkeep to reliability engineering, which in practice means working on BRIN and GIN indexing, PITR windows, Terraform-based DR configuration, and observability through RDS Performance Insights, rather than ASM striping, RMAN jobs, or static TKPROF traces. 

Disaster Recovery: Oracle vs. PostgreSQL 

The disaster recovery concepts carry across, but the mechanisms differ in ways worth understanding before you design your architecture. On replication modes, Oracle manages tiers strictly through policy using Maximum Protection, Maximum Availability, and Maximum Performance, whereas PostgreSQL configures the equivalent behaviour per transaction through synchronous_commit and its off, local, on, and remote_write settings. 

Read-scaling works differently too because Oracle Active Data Guard can route writes back to the primary automatically, while PostgreSQL Hot Standbys serve reads but can experience long-running queries conflicting with DDL and vacuuming on the primary, which is why you tune max_standby_streaming_delay. Failover orchestration presents another contrast, with Oracle relying on the Data Guard Broker and FSFO observers while RDS PostgreSQL leans on AWS control-plane orchestration and storage-level replication. Point-in-time recovery follows the same pattern: Oracle combines RMAN with Flashback Database, and PostgreSQL relies on continuous WAL archiving and base backups, which on RDS arrive as automated backups. 

Procedural Code and Query Translation 

Most of the translation work concentrates in procedural code and a handful of query constructs. PL/SQL packages convert into modular schema functions in PL/pgSQL, with the important caveat that packages and package-level state simply do not exist, meaning any logic depending on them has to be redesigned. The DBMS_* built-ins need the same treatment, replaced by native features, AWS services, or PostgreSQL extensions depending on what each one did. 

Hierarchical and legacy-join syntax is a more mechanical rewrite where CONNECT BY becomes WITH RECURSIVE CTEs, and the old (+) outer-join notation becomes standard ANSI JOINs. Index-Organized Tables and bitmap indexes have no direct equivalent, so you reach for BRIN, partial, or clustered indexes instead. 

Silent Data-Logic Traps (High Risk) 

The most dangerous category involves things that convert successfully on paper but behave differently afterwards. 

• The Empty String: Oracle treats ” as NULL, whereas PostgreSQL treats it as a genuine empty string, meaning any logic expecting ” IS NULL to be true will fail silently post-migration. 

• Dates: An Oracle DATE carries a time component, whereas a PostgreSQL DATE is strictly date-only, requiring you to map Oracle DATE to PostgreSQL TIMESTAMP to prevent losing the time portion. 

• Row Identifiers: An Oracle ROWID is stable, but a PostgreSQL ctid changes after VACUUM, so anything treating the row address as durable needs a thorough redesign. 

What Changes from Oracle to Postgres: The Concept Translation 

Before the command reference, it helps to see how physical and memory structures line up on RDS for PostgreSQL, alongside Oracle features that have no direct equivalent at all. 

Architectural Features Without Direct Equivalents 

Oracle Feature	PostgreSQL Reality	Action Required
RAC (Real Application Clusters)	No shared-everything model. Single writer + read replicas.	Shard writes, offload reads, or scale the writer vertically.
Autonomous Transactions	Not natively supported (PRAGMA AUTONOMOUS_TRANSACTION).	Use a loopback connection (dblink) or move the logic to the application.
Advanced Queuing (AQ) / Streams	Not available.	Migrate messaging to AWS SQS, Apache Kafka, or pgmq.
Flashback Query / Database	No native equivalent.	Rely on RDS PITR, standby lag, or logical snapshotting.

 

Architectural Mapping 

Oracle Component	RDS PostgreSQL Equivalent	Conceptual Difference
SGA (System Global Area)	shared_buffers + RDS storage	Restarting an RDS PostgreSQL instance clears shared_buffers, causing a temporary cold-cache effect while data reloads from storage.
PGA (Program Global Area)	work_mem + maintenance_work_mem	Allocated per-backend process. If work_mem = 64 MB and you have 100 active complex queries, that can consume up to 6.4 GB of RAM.
Datafiles / Tablespaces	Managed storage volume	RDS abstracts tablespaces away. CREATE TABLESPACE exists, but on RDS you manage storage as an EBS-backed volume AWS scales for you, meaning you do not lay out datafiles by hand.
Redo Logs / Archivelogs	WAL (Write-Ahead Logs)	PostgreSQL writes changes to the WAL before data files, then checkpoints them to storage: the same durability principle as redo, with different mechanics.

 

Command & Query Rosetta Stone 

Dictionary and Metadata Lookups 

Oracle DBA views are globally accessible through synonyms such as DBA_* and V$*, whereas PostgreSQL uses standard ANSI information_schema views or internal catalogs prefixed pg_. 

Administration Objective	Oracle Command / View	RDS PostgreSQL Equivalent
Show active sessions	SELECT * FROM v$session WHERE status=’ACTIVE’;	SELECT * FROM pg_stat_activity WHERE state=’active’;
Find table size	SELECT bytes FROM dba_segments WHERE segment_name=’X’;	SELECT pg_size_pretty(pg_total_relation_size(‘X’));
Kill a rogue session	ALTER SYSTEM KILL SESSION ‘sid,serial#’;	SELECT pg_terminate_backend(pid); (or pg_cancel_backend to stop query)
Show database version	SELECT * FROM v$version;	SELECT version();

 

DDL and System Modifications 

Operation	Oracle Syntax	RDS PostgreSQL Syntax
Modify parameter	ALTER SYSTEM SET open_cursors=300 SCOPE=BOTH;	Managed via AWS Parameter Groups (cluster or instance level); dynamic or reboot depending on parameter.
Change session parameter	ALTER SESSION SET CURRENT_SCHEMA = sales;	SET search_path TO sales, public;
Add auto-incrementing column	Sequence + trigger, or IDENTITY column	GENERATED BY DEFAULT AS IDENTITY

 

Data Type Mapping 

Oracle Type	PostgreSQL Equivalent	Note
VARCHAR2(n)	VARCHAR(n) / TEXT	PostgreSQL TEXT has no length penalty.
NUMBER(p,s)	NUMERIC(p,s)	For integers, prefer INT/BIGINT for performance.
NUMBER (no precision)	NUMERIC / DOUBLE PRECISION	Pick based on whether you need exact or floating-point values.
DATE (date + time)	TIMESTAMP	Oracle DATE carries a time component, so do not map it to DATE.
CLOB	TEXT	Large text handled natively.
BLOB	BYTEA	Or offload large binaries to S3.
RAW	BYTEA
VARCHAR2 boolean flags (‘Y’/’N’)	BOOLEAN	A good opportunity to clean up the data model.
Sequences + triggers	IDENTITY / SERIAL	Simpler, native auto-increment.

 

Procedural Code 

Oracle	PostgreSQL
PL/SQL packages (spec + body)	A schema of related PL/pgSQL functions; there is no package construct.
Packaged globals / state	Session settings, temp tables, or custom configuration.
DBMS_OUTPUT.PUT_LINE	RAISE NOTICE
Autonomous transactions	dblink loopback or application-level handling.

 

At Cevo, we help organisations confidently migrate complex Oracle workloads, overcome conversion limitations, and design scalable, high-performance database architectures on AWS. 

A successful transition to AWS RDS PostgreSQL goes beyond simple technical compatibility; it pairs reduced platform dependency and lower infrastructure overhead with a disciplined migration plan. By running an early AWS Schema Conversion Tool (SCT) assessment to clarify redesign workloads and testing silent data-logic traps (like empty-string NULL semantics) well before cutover, we ensure your team’s deep database expertise remains a key asset throughout the journey. 

Partnering with Cevo delivers a migration strategy that balances technical feasibility with a credible total-cost advantage, low delivery risk, and measurable gains in agility and resilience.

Get in touch with our team today to discuss your database migration strategy in detail.